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1.  Identification of cancer genomic markers via integrative sparse boosting 
Biostatistics (Oxford, England)  2012;13(3):509-522.
In high-throughput cancer genomic studies, markers identified from the analysis of single data sets often suffer a lack of reproducibility because of the small sample sizes. An ideal solution is to conduct large-scale prospective studies, which are extremely expensive and time consuming. A cost-effective remedy is to pool data from multiple comparable studies and conduct integrative analysis. Integrative analysis of multiple data sets is challenging because of the high dimensionality of genomic measurements and heterogeneity among studies. In this article, we propose a sparse boosting approach for marker identification in integrative analysis of multiple heterogeneous cancer diagnosis studies with gene expression measurements. The proposed approach can effectively accommodate the heterogeneity among multiple studies and identify markers with consistent effects across studies. Simulation shows that the proposed approach has satisfactory identification results and outperforms alternatives including an intensity approach and meta-analysis. The proposed approach is used to identify markers of pancreatic cancer and liver cancer.
doi:10.1093/biostatistics/kxr033
PMCID: PMC3577103  PMID: 22045909
Cancer genomics; Marker identification; Sparse boosting
2.  Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer 
PLoS Genetics  2015;11(12):e1005689.
Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease conditions.
Author Summary
At present, it is already possible to generate different type of omics–high throughput–data in the same individuals. However, we lack methodology to adequately combine them. Many challenges arise while the amount of data increases and we need to find the way to identify and understand the complex relationships when integrating data. In this regard, new statistical approaches are needed, such as the ones we propose and apply here to integrate three types of omics data (genomics, epigenomics, and transcriptomics) generated using bladder cancer tumor samples. These innovative approaches (LASSO and ENET combined with a permutation-based MaxT method) allowed us to find 48 genes whose expression levels were significantly associated with genomics and epigenomics markers. The adequacy of this approach was confirmed by the use of an independent data set from The Cancer Genome Atlas Consortium: 75% of the genes were replicated. Previous sound biological evidences further support the results obtained.
doi:10.1371/journal.pgen.1005689
PMCID: PMC4672920  PMID: 26646822
3.  Identification of Breast Cancer Prognosis Markers via Integrative Analysis 
Summary
In breast cancer research, it is of great interest to identify genomic markers associated with prognosis. Multiple gene profiling studies have been conducted for such a purpose. Genomic markers identified from the analysis of single datasets often do not have satisfactory reproducibility. Among the multiple possible reasons, the most important one is the small sample sizes of individual studies. A cost-effective solution is to pool data from multiple comparable studies and conduct integrative analysis. In this study, we collect four breast cancer prognosis studies with gene expression measurements. We describe the relationship between prognosis and gene expressions using the accelerated failure time (AFT) models. We adopt a 2-norm group bridge penalization approach for marker identification. This integrative analysis approach can effectively identify markers with consistent effects across multiple datasets and naturally accommodate the heterogeneity among studies. Statistical and simulation studies demonstrate satisfactory performance of this approach. Breast cancer prognosis markers identified using this approach have sound biological implications and satisfactory prediction performance.
doi:10.1016/j.csda.2012.02.017
PMCID: PMC3389801  PMID: 22773869
Breast cancer prognosis; Gene expression; Marker identification; Integrative analysis; 2-norm group bridge
4.  Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization 
Summary
In cancer diagnosis studies, high-throughput gene profiling has been extensively conducted, searching for genes whose expressions may serve as markers. Data generated from such studies have the “large d, small n” feature, with the number of genes profiled much larger than the sample size. Penalization has been extensively adopted for simultaneous estimation and marker selection. Because of small sample sizes, markers identified from the analysis of single datasets can be unsatisfactory. A cost-effective remedy is to conduct integrative analysis of multiple heterogeneous datasets. In this article, we investigate composite penalization methods for estimation and marker selection in integrative analysis. The proposed methods use the minimax concave penalty (MCP) as the outer penalty. Under the homogeneity model, the ridge penalty is adopted as the inner penalty. Under the heterogeneity model, the Lasso penalty and MCP are adopted as the inner penalty. Effective computational algorithms based on coordinate descent are developed. Numerical studies, including simulation and analysis of practical cancer datasets, show satisfactory performance of the proposed methods.
doi:10.1111/j.1467-9469.2012.00816.x
PMCID: PMC3933169  PMID: 24578589
cancer diagnosis studies; composite penalization; gene expression; integrative analysis
5.  Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer 
Evaluation of survival models to predict cancer patient prognosis is one of the most important areas of emphasis in cancer research. A binary classification approach has difficulty directly predicting survival due to the characteristics of censored observations and the fact that the predictive power depends on the threshold used to set two classes. In contrast, the traditional Cox regression approach has some drawbacks in the sense that it does not allow for the identification of interactions between genomic features, which could have key roles associated with cancer prognosis. In addition, data integration is regarded as one of the important issues in improving the predictive power of survival models since cancer could be caused by multiple alterations through meta-dimensional genomic data including genome, epigenome, transcriptome, and proteome. Here we have proposed a new integrative framework designed to perform these three functions simultaneously: (1) predicting censored survival data; (2) integrating meta-dimensional omics data; (3) identifying interactions within/between meta-dimensional genomic features associated with survival. In order to predict censored survival time, martingale residuals were calculated as a new continuous outcome and a new fitness function used by the grammatical evolution neural network (GENN) based on mean absolute difference of martingale residuals was implemented. To test the utility of the proposed framework, a simulation study was conducted, followed by an analysis of meta-dimensional omics data including copy number, gene expression, DNA methylation, and protein expression data in breast cancer retrieved from The Cancer Genome Atlas (TCGA). On the basis of the results from breast cancer dataset, we were able to identify interactions not only within a single dimension of genomic data but also between meta-dimensional omics data that are associated with survival. Notably, the predictive power of our best meta-dimensional model was 73% which outperformed all of the other models conducted based on a single dimension of genomic data. Breast cancer is an extremely heterogeneous disease and the high levels of genomic diversity within/between breast tumors could affect the risk of therapeutic responses and disease progression. Thus, identifying interactions within/between meta-dimensional omics data associated with survival in breast cancer is expected to deliver direction for improved meta-dimensional prognostic biomarkers and therapeutic targets.
Graphical Abstract
doi:10.1016/j.jbi.2015.05.019
PMCID: PMC4550096  PMID: 26048077
Survival prediction; Data integration; Interaction between multi-omics data; TCGA; Breast cancer
6.  Revealing a signaling role of phytosphingosine-1-phosphate in yeast 
Perturbing metabolic systems of bioactive sphingolipids with genetic approachMultiple types of “omics” data collected from the systemSystems approach for integrating multiple “omics” informationPredicting signal transduction information flow: lipid; TF activation; gene expression
In contemporary biomedical research, gene mutation remains the most powerful and commonly used tool in molecular and systems biology for perturbation and dissection of biological systems. However, as biological systems consist of highly connected networks, for example, metabolic networks or signal transduction networks, perturbing one portion could result in widely spread effects across the network. Such ‘ripple effects' in systems pose a challenge to the paradigm of investigating the role of a metabolite through mutating enzymes required for its production. In this study, we have developed a systems biology approach that integrates different types of ‘-omics' data to identify signal transduction pathways involving spingolipids and gene expression. See Figure 1 for an overall scheme of our approaches.
Sphingolipids are a family of bioactive lipids that have important signaling functions in cells; in yeast, de novo synthesis is required to mediate the cell response to heat shock. We hypothesized that a specific sphingolipid, phyto-sphingosine-1-phosphate (PHS1P), functions as a signaling molecule in the heat stress response (HSR) because, though its mammalian counterparts are known to have important signaling roles, the function of this metabolite in yeast remains unknown. To identify a putative role of PHS1P in the HSR, we deleted the genes involved in production (LCB4 and LCB5) and degradation (DPL1) of PHS1P to perturb its levels in cells. In wild-type cells, heat shock induces a significant increase in PHS1P. Over the same course, expression of over a thousand genes was modulated.
While deleting the genes involved in PHS1P metabolism ‘clamped' the PHS1P concentration as expected, these mutations also resulted in wide spread changes in many sphingolipids in addition to PHS1P. This ‘ripple effect' prevented direct identification of signaling role of PHS1P in gene expression. We overcame this difficulty by using a set of systems approaches as follows: (1) identifying the information between levels of each individual sphingolipid species and gene expression through combining correlation analysis and clustering; (2) identifying the putative PHS1P-sensitive subset of genes by analyzing the results from step 1; (3) identifying transcription factors (TFs) that potentially regulate these PHS1P-sensitive genes thought promoter analysis; (4) modeling the activation states of the TFs by combining gene expression data and promoter sequence data; and finally, (5) modeling the relationship between sphingolipids and activation of TFs.
Our study showed that 441 genes were differentially expressed in the lcb4Δ/lcb5Δ strain in comparison to wild-type strain; however, only 77 genes among them showed a significant correlation with respect to PHS1P, with 22 genes positively correlated and 54 genes negatively correlated. The results led to a hypothesis that the genes showing significant correlation were PHS1P sensitive whereas differential expression of other genes resulted from the compounding ‘ripple effects' of the gene deletions. We tested this hypothesis by directly treating cells with PHS1P and monitoring the expression levels of the genes that were PHS1P sensitive and PHS1P insensitive, and the results showed that the expression of PHS1P-sensitive genes indeed changed in response to the treatment whereas others did not. We developed a statistical model referred to as Bayesian transcription factor state model to infer activation states of TFs in cells under a specific condition based on the genomic information and gene expression data. We then used a Bayesian logistic regression to further model the relationship between the lipid concentrations and activation states of the TFs. Combined TF enrichment analysis and TF state modeling indicated that the HAP TF complex was likely responding to the signal from PHS1P and mediating the regulation of PHS1P-sensitive genes. We tested this hypothesis by treating wild type and a strain of yeast with deletion of HAP4 gene (hap4Δ), a component of the HAP complex, with PHS1P and monitoring the expression of PHS1P-sensitive genes. Indeed, the PHS1P induced the genes in the wild-type strain but not in hap4Δ, thus indicating that induction of the PHS1P-sensitive genes required a functioning HAP complex (see Figure 5 ).
In summary, our experiments demonstrated that, though gene mutation remains one of the most powerful tools to perturb biological systems, the high connectivity of biological systems poses a challenge for using this approach to identify signaling roles of bioactive metabolites. Here, we demonstrated combining the information from multiple types of ‘-omics' data using systems approaches, it is possible to circumvent these difficulties and reveal novel signal transduction pathways.
Sphingolipids including sphingosine-1-phosphate and ceramide participate in numerous cell programs through signaling mechanisms. This class of lipids has important functions in stress responses; however, determining which sphingolipid mediates specific events has remained encumbered by the numerous metabolic interconnections of sphingolipids, such that modulating a specific lipid of interest through manipulating metabolic enzymes causes ‘ripple effects', which change levels of many other lipids. Here, we develop a method of integrative analysis for genomic, transcriptomic, and lipidomic data to address this previously intractable problem. This method revealed a specific signaling role for phytosphingosine-1-phosphate, a lipid with no previously defined specific function in yeast, in regulating genes required for mitochondrial respiration through the HAP complex transcription factor. This approach could be applied to extract meaningful biological information from a similar experimental design that produces multiple sets of high-throughput data.
doi:10.1038/msb.2010.3
PMCID: PMC2835565  PMID: 20160710
information integration; lipidomics; signal transduction; sphingolipids; transcriptomics
7.  Integrative analysis of multiple cancer genomic datasets under the heterogeneity model 
Statistics in medicine  2013;32(20):3509-3521.
In the analysis of cancer studies with high-dimensional genomic measurements, integrative analysis provides an effective way of pooling information across multiple heterogeneous datasets. The genomic basis of multiple independent datasets, which can be characterized by the sets of genomic markers, can be described using the homogeneity model or heterogeneity model. Under the homogeneity model, all datasets share the same set of markers associated with responses. In contrast, under the heterogeneity model, different studies have overlapping but possibly different sets of markers. The heterogeneity model contains the homogeneity model as a special case and can be much more flexible. Marker selection under the heterogeneity model calls for bi-level selection to determine whether a covariate is associated with response in any study at all as well as in which studies it is associated with responses. In this study, we consider two minimax concave penalty (MCP) based penalization approaches for marker selection under the heterogeneity model. For each approach, we describe its rationale and an effective computational algorithm. We conduct simulation to investigate their performance and compare with the existing alternatives. We also apply the proposed approaches to the analysis of gene expression data on multiple cancers.
doi:10.1002/sim.5780
PMCID: PMC3743947  PMID: 23519988
Integrative analysis; Heterogeneity model; Marker selection
8.  Identification of Breast Cancer Prognosis Markers using Integrative Sparse Boosting 
Summary
Objectives
In breast cancer research, it is important to identify genomic markers associated with prognosis. Multiple microarray gene expression profiling studies have been conducted, searching for prognosis markers. Genomic markers identified from the analysis of single datasets often suffer a lack of reproducibility because of small sample sizes. Integrative analysis of data from multiple independent studies has a larger sample size and may provide a cost-effective solution.
Methods
We collect four breast cancer prognosis studies with gene expression measurements. An accelerated failure time (AFT) model with an unknown error distribution is adopted to describe survival. An integrative sparse boosting approach is employed for marker selection. The proposed model and boosting approach can effectively accommodate heterogeneity across multiple studies and identify genes with consistent effects.
Results
Simulation study shows that the proposed approach outperforms alternatives including meta-analysis and intensity approaches by identifying the majority or all of the true positives, while having a low false positive rate. In the analysis of breast cancer data, 44 genes are identified as associated with prognosis. Many of the identified genes have been previously suggested as associated with tumorigenesis and cancer prognosis. The identified genes and corresponding predicted risk scores differ from those using alternative approaches. Monte Carlo-based prediction evaluation suggests that the proposed approach has the best prediction performance.
Conclusions
Integrative analysis may provide an effective way of identifying breast cancer prognosis markers. Markers identified using the integrative sparse boosting analysis have sound biological implications and satisfactory prediction performance.
doi:10.3414/ME11-02-0019
PMCID: PMC3598607  PMID: 22344268
Breast cancer prognosis; Gene Expression; Integrative analysis; Sparse boosting
9.  Integrative analysis of multiple cancer prognosis studies with gene expression measurements 
Statistics in Medicine  2011;30(28):3361-3371.
Although in cancer research microarray gene profiling studies have been successful in identifying genetic variants predisposing to the development and progression of cancer, the identified markers from analysis of single datasets often suffer low reproducibility. Among multiple possible causes, the most important one is the small sample size hence the lack of power of single studies. Integrative analysis jointly considers multiple heterogeneous studies, has a significantly larger sample size, and can improve reproducibility. In this article, we focus on cancer prognosis studies, where the response variables are progression-free, overall, or other types of survival. A group minimax concave penalty (GMCP) penalized integrative analysis approach is proposed for analyzing multiple heterogeneous cancer prognosis studies with microarray gene expression measurements. An efficient group coordinate descent algorithm is developed. The GMCP can automatically accommodate the heterogeneity across multiple datasets, and the identified markers have consistent effects across multiple studies. Simulation studies show that the GMCP provides significantly improved selection results as compared with the existing meta-analysis approaches, intensity approaches, and group Lasso penalized integrative analysis. We apply the GMCP to four microarray studies and identify genes associated with the prognosis of breast cancer.
doi:10.1002/sim.4337
PMCID: PMC3399910  PMID: 22105693
integrative analysis; cancer prognosis; microarray; penalized selection
10.  Incorporating Network Structure in Integrative Analysis of Cancer Prognosis Data 
Genetic epidemiology  2012;37(2):173-183.
In high-throughput cancer genomic studies, markers identified from the analysis of single datasets may have unsatisfactory properties because of low sample sizes. Integrative analysis pools and analyzes raw data from multiple studies, and can effectively increase sample size and lead to improved marker identification results. In this study, we consider the integrative analysis of multiple high-throughput cancer prognosis studies. In the existing integrative analysis studies, the interplay among genes, which can be described using the network structure, has not been effectively accounted for. In network analysis, tightly-connected nodes (genes) are more likely to have related biological functions and similar regression coefficients. The goal of this study is to develop an analysis approach that can incorporate the gene network structure in integrative analysis. To this end, we adopt an AFT (accelerated failure time) model to describe survival. A weighted least squares approach, which has low computational cost, is adopted for estimation. For marker selection, we propose a new penalization approach. The proposed penalty is composed of two parts. The first part is a group MCP penalty, and conducts gene selection. The second part is a Laplacian penalty, and smoothes the differences of coefficients for tightly-connected genes. A group coordinate descent approach is developed to compute the proposed estimate. Simulation study shows satisfactory performance of the proposed approach when there exist moderate to strong correlations among genes. We analyze three lung cancer prognosis datasets, and demonstrate that incorporating the network structure can lead to the identification of important genes and improved prediction performance.
doi:10.1002/gepi.21697
PMCID: PMC3909475  PMID: 23161517
Integrative analysis; Cancer prognosis; Gene network; Penalized selection; Laplacian shrinkage
11.  Integrative Analysis of High-throughput Cancer Studies with Contrasted Penalization 
Genetic epidemiology  2014;38(2):144-151.
In cancer studies with high-throughput genetic and genomic measurements, integrative analysis provides a way to effectively pool and analyze heterogeneous raw data from multiple independent studies and outperforms “classic” meta-analysis and single-dataset analysis. When marker selection is of interest, the genetic basis of multiple datasets can be described using the homogeneity model or the heterogeneity model. In this study, we consider marker selection under the heterogeneity model, which includes the homogeneity model as a special case and can be more flexible. Penalization methods have been developed in the literature for marker selection. This study advances from the published ones by introducing the contrast penalties, which can accommodate the within- and across-dataset structures of covariates/regression coefficients and, by doing so, further improve marker selection performance. Specifically, we develop a penalization method that accommodates the across-dataset structures by smoothing over regression coefficients. An effective iterative algorithm, which calls an inner coordinate descent iteration, is developed. Simulation shows that the proposed method outperforms the benchmark with more accurate marker identification. The analysis of breast cancer and lung cancer prognosis studies with gene expression measurements shows that the proposed method identifies genes different from those using the benchmark and has better prediction performance.
doi:10.1002/gepi.21781
PMCID: PMC4355402  PMID: 24395534
Integrative analysis; Contrasted penalization; Marker selection; High-throughput cancer studies
12.  Sparse Group Penalized Integrative Analysis of Multiple Cancer Prognosis Datasets 
Genetics research  2013;95(0):68-77.
SUMMARY
In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Because of the “large d, small n” characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyzes multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the AFT (accelerated failure time) model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group MCP (minimax concave penalty) approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach.
doi:10.1017/S0016672313000086
PMCID: PMC4090387  PMID: 23938111
Integrative analysis; Cancer prognosis; Heterogeneity model; Penalization
13.  Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification 
BMC Genomics  2015;16:1022.
Background
One major goal of large-scale cancer omics study is to identify molecular subtypes for more accurate cancer diagnoses and treatments. To deal with high-dimensional cancer multi-omics data, a promising strategy is to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data.
Results
In this study, we proposed a novel low-rank approximation based integrative probabilistic model to fast find the shared principal subspace across multiple data types: the convexity of the low-rank regularized likelihood function of the probabilistic model ensures efficient and stable model fitting. Candidate molecular subtypes can be identified by unsupervised clustering hundreds of cancer samples in the reduced low-dimensional subspace. On testing datasets, our method LRAcluster (low-rank approximation based multi-omics data clustering) runs much faster with better clustering performances than the existing method. Then, we applied LRAcluster on large-scale cancer multi-omics data from TCGA. The pan-cancer analysis results show that the cancers of different tissue origins are generally grouped as independent clusters, except squamous-like carcinomas. While the single cancer type analysis suggests that the omics data have different subtyping abilities for different cancer types.
Conclusions
LRAcluster is a very useful method for fast dimension reduction and unsupervised clustering of large-scale multi-omics data. LRAcluster is implemented in R and freely available via http://bioinfo.au.tsinghua.edu.cn/software/lracluster/.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-2223-8) contains supplementary material, which is available to authorized users.
doi:10.1186/s12864-015-2223-8
PMCID: PMC4667498  PMID: 26626453
Mutli-omics; Cancer; Low-rank approximation; Clustering; Dimension reduction; Algorithm
14.  An integrative imputation method based on multi-omics datasets 
BMC Bioinformatics  2016;17:247.
Background
Integrative analysis of multi-omics data is becoming increasingly important to unravel functional mechanisms of complex diseases. However, the currently available multi-omics datasets inevitably suffer from missing values due to technical limitations and various constrains in experiments. These missing values severely hinder integrative analysis of multi-omics data. Current imputation methods mainly focus on using single omics data while ignoring biological interconnections and information imbedded in multi-omics data sets.
Results
In this study, a novel multi-omics imputation method was proposed to integrate multiple correlated omics datasets for improving the imputation accuracy. Our method was designed to: 1) combine the estimates of missing value from individual omics data itself as well as from other omics, and 2) simultaneously impute multiple missing omics datasets by an iterative algorithm. We compared our method with five imputation methods using single omics data at different noise levels, sample sizes and data missing rates. The results demonstrated the advantage and efficiency of our method, consistently in terms of the imputation error and the recovery of mRNA-miRNA network structure.
Conclusions
We concluded that our proposed imputation method can utilize more biological information to minimize the imputation error and thus can improve the performance of downstream analysis such as genetic regulatory network construction.
Electronic supplementary material
The online version of this article (doi:10.1186/s12859-016-1122-6) contains supplementary material, which is available to authorized users.
doi:10.1186/s12859-016-1122-6
PMCID: PMC4915152  PMID: 27329642
Multi-omics data; Imputation; Integrative analysis; Ensemble learning
15.  SPARSE INTEGRATIVE CLUSTERING OF MULTIPLE OMICS DATA SETS 
The annals of applied statistics  2012;7(1):269-294.
High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation, and gene expression associated with a disease. An integrated genomic profiling approach measuring multiple omics data types simultaneously in the same set of biological samples would render an integrated data resolution that would not be available with any single data type. In this study, we use penalized latent variable regression methods for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient samples into biologically and clinically relevant disease subtypes. We consider lasso (Tibshirani, 1996), elastic net (Zou and Hastie, 2005), and fused lasso (Tibshirani et al., 2005) methods to induce sparsity in the coefficient vectors, revealing important genomic features that have significant contributions to the latent variables. An iterative ridge regression is used to compute the sparse coefficient vectors. In model selection, a uniform design (Fang and Wang, 1994) is used to seek “experimental” points that scattered uniformly across the search domain for efficient sampling of tuning parameter combinations. We compared our method to sparse singular value decomposition (SVD) and penalized Gaussian mixture model (GMM) using both real and simulated data sets. The proposed method is applied to integrate genomic, epigenomic, and transcriptomic data for subtype analysis in breast and lung cancer data sets.
doi:10.1214/12-AOAS578
PMCID: PMC3935438  PMID: 24587839
16.  AucPR: An AUC-based approach using penalized regression for disease prediction with high-dimensional omics data 
BMC Genomics  2014;15(Suppl 10):S1.
Motivation
It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data.
Results
We propose an AUC-based approach using penalized regression (AucPR), which is a parametric method used for obtaining a linear combination for maximizing the AUC. To obtain the AUC maximizer in a high-dimensional context, we transform a classical parametric AUC maximizer, which is used in a low-dimensional context, into a regression framework and thus, apply the penalization regression approach directly. Two kinds of penalization, lasso and elastic net, are considered. The parametric approach can avoid some of the difficulties of a conventional non-parametric AUC-based approach, such as the lack of an appropriate concave objective function and a prudent choice of the smoothing parameter. We apply the proposed AucPR for gene selection and classification using four real microarray and synthetic data. Through numerical studies, AucPR is shown to perform better than the penalized logistic regression and the nonparametric AUC-based method, in the sense of AUC and sensitivity for a given specificity, particularly when there are many correlated genes.
Conclusion
We propose a powerful parametric and easily-implementable linear classifier AucPR, for gene selection and disease prediction for high-dimensional data. AucPR is recommended for its good prediction performance. Beside gene expression microarray data, AucPR can be applied to other types of high-dimensional omics data, such as miRNA and protein data.
doi:10.1186/1471-2164-15-S10-S1
PMCID: PMC4304290  PMID: 25559769
AUC; high-dimensional data; penalized regression; ROC curve
17.  Integrative clustering methods for high-dimensional molecular data 
Translational cancer research  2014;3(3):202-216.
High-throughput ‘omic’ data, such as gene expression, DNA methylation, DNA copy number, has played an instrumental role in furthering our understanding of the molecular basis in states of human health and disease. As cells with similar morphological characteristics can exhibit entirely different molecular profiles and because of the potential that these discrepancies might further our understanding of patient-level variability in clinical outcomes, there is significant interest in the use of high-throughput ‘omic’ data for the identification of novel molecular subtypes of a disease. While numerous clustering methods have been proposed for identifying of molecular subtypes, most were developed for single “omic’ data types and may not be appropriate when more than one ‘omic’ data type are collected on study subjects. Given that complex diseases, such as cancer, arise as a result of genomic, epigenomic, transcriptomic, and proteomic alterations, integrative clustering methods for the simultaneous clustering of multiple ‘omic’ data types have great potential to aid in molecular subtype discovery. Traditionally, ad hoc manual data integration has been performed using the results obtained from the clustering of individual ‘omic’ data types on the same set of patient samples. However, such methods often result in inconsistent assignment of subjects to the molecular cancer subtypes. Recently, several methods have been proposed in the literature that offers a rigorous framework for the simultaneous integration of multiple ‘omic’ data types in a single comprehensive analysis. In this paper, we present a systematic review of existing integrative clustering methods.
doi:10.3978/j.issn.2218-676X.2014.06.03
PMCID: PMC4166480  PMID: 25243110
Consensus clustering; cophenetic correlation; latent models; mixture models; non-negative matrix factorization
18.  Integrative Analysis Using Module-Guided Random Forests Reveals Correlated Genetic Factors Related to Mouse Weight 
PLoS Computational Biology  2013;9(3):e1002956.
Complex traits such as obesity are manifestations of intricate interactions of multiple genetic factors. However, such relationships are difficult to identify. Thanks to the recent advance in high-throughput technology, a large amount of data has been collected for various complex traits, including obesity. These data often measure different biological aspects of the traits of interest, including genotypic variations at the DNA level and gene expression alterations at the RNA level. Integration of such heterogeneous data provides promising opportunities to understand the genetic components and possibly genetic architecture of complex traits. In this paper, we propose a machine learning based method, module-guided Random Forests (mgRF), to integrate genotypic and gene expression data to investigate genetic factors and molecular mechanism underlying complex traits. mgRF is an augmented Random Forests method enhanced by a network analysis for identifying multiple correlated variables of different types. We applied mgRF to genetic markers and gene expression data from a cohort of F2 female mouse intercross. mgRF outperformed several existing methods in our extensive comparison. Our new approach has an improved performance when combining both genotypic and gene expression data compared to using either one of the two types of data alone. The resulting predictive variables identified by mgRF provide information of perturbed pathways that are related to body weight. More importantly, the results uncovered intricate interactions among genetic markers and genes that have been overlooked if only one type of data was examined. Our results shed light on genetic mechanisms of obesity and our approach provides a promising complementary framework to the “genetics of gene expression” analysis for integrating genotypic and gene expression information for analyzing complex traits.
Author Summary
Obesity has become a perilous global epidemic that can lead to complex diseases, such as diabetes and cardiovascular diseases. Much effort has been devoted to the studies of the genetic mechanisms that pillow the manifestation of obesity. Although a large quantity of experimental data has been accumulated lately using high-throughput techniques, our understanding of genetic mechanisms of obesity is still limited. The proposed method is motivated to address three critical issues that have impeded the existing methods. The first is the curse of dimensionality in selecting a subset of genetic elements related to the traits of interest from a large number of candidates. The second is genetic multiplicity underlying non-Mendelian traits, in which multiple genes are in interplay. The third issue is the integration of data from multiple sources in light of genetic multiplicity and curse of dimensionality. Here, we propose a new method, which augments the Random Forests method with a network-based analysis, to integrate genotypic and gene expression information and identify correlated multiple genetic elements underlying mouse weight. Our results shed light on complex genetic interactions underlying obesity, which can form viable hypotheses worthy of further investigation.
doi:10.1371/journal.pcbi.1002956
PMCID: PMC3591263  PMID: 23505362
19.  A multivariate approach to the integration of multi-omics datasets 
BMC Bioinformatics  2014;15:162.
Background
To leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets. Based on a covariance optimization criterion, MCIA simultaneously projects several datasets into the same dimensional space, transforming diverse sets of features onto the same scale, to extract the most variant from each dataset and facilitate biological interpretation and pathway analysis.
Results
We demonstrate integration of multiple layers of information using MCIA, applied to two typical “omics” research scenarios. The integration of transcriptome and proteome profiles of cells in the NCI-60 cancer cell line panel revealed distinct, complementary features, which together increased the coverage and power of pathway analysis. Our analysis highlighted the importance of the leukemia extravasation signaling pathway in leukemia that was not highly ranked in the analysis of any individual dataset. Secondly, we compared transcriptome profiles of high grade serous ovarian tumors that were obtained, on two different microarray platforms and next generation RNA-sequencing, to identify the most informative platform and extract robust biomarkers of molecular subtypes. We discovered that the variance of RNA-sequencing data processed using RPKM had greater variance than that with MapSplice and RSEM. We provided novel markers highly associated to tumor molecular subtype combined from four data platforms. MCIA is implemented and available in the R/Bioconductor “omicade4” package.
Conclusion
We believe MCIA is an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals. The method is not dependent on feature annotation, and thus it can extract important features even when there are not present across all datasets. MCIA provides simple graphical representations for the identification of relationships between large datasets.
doi:10.1186/1471-2105-15-162
PMCID: PMC4053266  PMID: 24884486
Multivariate analysis; Multiple co-inertia; Data integration; Omic data; Visualization
20.  From Sample to Multi-Omics Conclusions in under 48 Hours 
mSystems  2016;1(2):e00038-16.
Polymicrobial infections are difficult to diagnose due to the challenge in comprehensively cultivating the microbes present. Omics methods, such as 16S rRNA sequencing, metagenomics, and metabolomics, can provide a more complete picture of a microbial community and its metabolite production, without the biases and selectivity of microbial culture. However, these advanced methods have not been applied to clinical or industrial microbiology or other areas where complex microbial dysbioses require immediate intervention. The reason for this is the length of time required to generate and analyze omics data. Here, we describe the development and application of a pipeline for multi-omics data analysis in time frames matching those of the culture-based approaches often used for these applications. This study applied multi-omics methods effectively in clinically relevant time frames and sets a precedent toward their implementation in clinical medicine and industrial microbiology.
ABSTRACT
Multi-omics methods have greatly advanced our understanding of the biological organism and its microbial associates. However, they are not routinely used in clinical or industrial applications, due to the length of time required to generate and analyze omics data. Here, we applied a novel integrated omics pipeline for the analysis of human and environmental samples in under 48 h. Human subjects that ferment their own foods provided swab samples from skin, feces, oral cavity, fermented foods, and household surfaces to assess the impact of home food fermentation on their microbial and chemical ecology. These samples were analyzed with 16S rRNA gene sequencing, inferred gene function profiles, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics through the Qiita, PICRUSt, and GNPS pipelines, respectively. The human sample microbiomes clustered with the corresponding sample types in the American Gut Project (http://www.americangut.org), and the fermented food samples produced a separate cluster. The microbial communities of the household surfaces were primarily sourced from the fermented foods, and their consumption was associated with increased gut microbial diversity. Untargeted metabolomics revealed that human skin and fermented food samples had separate chemical ecologies and that stool was more similar to fermented foods than to other sample types. Metabolites from the fermented foods, including plant products such as procyanidin and pheophytin, were present in the skin and stool samples of the individuals consuming the foods. Some food metabolites were modified during digestion, and others were detected in stool intact. This study represents a first-of-its-kind analysis of multi-omics data that achieved time intervals matching those of classic microbiological culturing.
IMPORTANCE Polymicrobial infections are difficult to diagnose due to the challenge in comprehensively cultivating the microbes present. Omics methods, such as 16S rRNA sequencing, metagenomics, and metabolomics, can provide a more complete picture of a microbial community and its metabolite production, without the biases and selectivity of microbial culture. However, these advanced methods have not been applied to clinical or industrial microbiology or other areas where complex microbial dysbioses require immediate intervention. The reason for this is the length of time required to generate and analyze omics data. Here, we describe the development and application of a pipeline for multi-omics data analysis in time frames matching those of the culture-based approaches often used for these applications. This study applied multi-omics methods effectively in clinically relevant time frames and sets a precedent toward their implementation in clinical medicine and industrial microbiology.
doi:10.1128/mSystems.00038-16
PMCID: PMC5069746  PMID: 27822524
16S rRNA; microbiome; fermented food; metabolome; molecular networking; rapid response
21.  Analysis of Genome-Wide Association Studies with Multiple Outcomes Using Penalization 
PLoS ONE  2012;7(12):e51198.
Genome-wide association studies have been extensively conducted, searching for markers for biologically meaningful outcomes and phenotypes. Penalization methods have been adopted in the analysis of the joint effects of a large number of SNPs (single nucleotide polymorphisms) and marker identification. This study is partly motivated by the analysis of heterogeneous stock mice dataset, in which multiple correlated phenotypes and a large number of SNPs are available. Existing penalization methods designed to analyze a single response variable cannot accommodate the correlation among multiple response variables. With multiple response variables sharing the same set of markers, joint modeling is first employed to accommodate the correlation. The group Lasso approach is adopted to select markers associated with all the outcome variables. An efficient computational algorithm is developed. Simulation study and analysis of the heterogeneous stock mice dataset show that the proposed method can outperform existing penalization methods.
doi:10.1371/journal.pone.0051198
PMCID: PMC3522680  PMID: 23272092
22.  Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes 
BMC Genomics  2015;16:924.
Background
The increased multi-omics information on carefully phenotyped patients in studies of complex diseases requires novel methods for data integration. Unlike continuous intensity measurements from most omics data sets, phenome data contain clinical variables that are binary, ordinal and categorical.
Results
In this paper we introduce an integrative phenotyping framework (iPF) for disease subtype discovery. A feature topology plot was developed for effective dimension reduction and visualization of multi-omics data. The approach is free of model assumption and robust to data noises or missingness. We developed a workflow to integrate homogeneous patient clustering from different omics data in an agglomerative manner and then visualized heterogeneous clustering of pairwise omics sources. We applied the framework to two batches of lung samples obtained from patients diagnosed with chronic obstructive lung disease (COPD) or interstitial lung disease (ILD) with well-characterized clinical (phenomic) data, mRNA and microRNA expression profiles. Application of iPF to the first training batch identified clusters of patients consisting of homogenous disease phenotypes as well as clusters with intermediate disease characteristics. Analysis of the second batch revealed a similar data structure, confirming the presence of intermediate clusters. Genes in the intermediate clusters were enriched with inflammatory and immune functional annotations, suggesting that they represent mechanistically distinct disease subphenotypes that may response to immunomodulatory therapies. The iPF software package and all source codes are publicly available.
Conclusions
Identification of subclusters with distinct clinical and biomolecular characteristics suggests that integration of phenomic and other omics information could lead to identification of novel mechanism-based disease sub-phenotypes.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-2170-4) contains supplementary material, which is available to authorized users.
doi:10.1186/s12864-015-2170-4
PMCID: PMC4642618  PMID: 26560100
Cluster analysis; Genomics; Chronic lung disease; Integrative clustering
23.  3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data 
BMC Systems Biology  2013;7:64.
Background
Integrative and comparative analyses of multiple transcriptomics, proteomics and metabolomics datasets require an intensive knowledge of tools and background concepts. Thus, it is challenging for users to perform such analyses, highlighting the need for a single tool for such purposes. The 3Omics one-click web tool was developed to visualize and rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining five commonly used analyses: correlation networking, coexpression, phenotyping, pathway enrichment, and GO (Gene Ontology) enrichment.
Results
3Omics generates inter-omic correlation networks to visualize relationships in data with respect to time or experimental conditions for all transcripts, proteins and metabolites. If only two of three omics datasets are input, then 3Omics supplements the missing transcript, protein or metabolite information related to the input data by text-mining the PubMed database. 3Omics’ coexpression analysis assists in revealing functions shared among different omics datasets. 3Omics’ phenotype analysis integrates Online Mendelian Inheritance in Man with available transcript or protein data. Pathway enrichment analysis on metabolomics data by 3Omics reveals enriched pathways in the KEGG/HumanCyc database. 3Omics performs statistical Gene Ontology-based functional enrichment analyses to display significantly overrepresented GO terms in transcriptomic experiments. Although the principal application of 3Omics is the integration of multiple omics datasets, it is also capable of analyzing individual omics datasets. The information obtained from the analyses of 3Omics in Case Studies 1 and 2 are also in accordance with comprehensive findings in the literature.
Conclusions
3Omics incorporates the advantages and functionality of existing software into a single platform, thereby simplifying data analysis and enabling the user to perform a one-click integrated analysis. Visualization and analysis results are downloadable for further user customization and analysis. The 3Omics software can be freely accessed at http://3omics.cmdm.tw.
doi:10.1186/1752-0509-7-64
PMCID: PMC3723580  PMID: 23875761
Visualization; Omics integration; Systems biology; Transcriptomics; Proteomics; Metabolomics; Analysis
24.  25th Annual Computational Neuroscience Meeting: CNS-2016 
Sharpee, Tatyana O. | Destexhe, Alain | Kawato, Mitsuo | Sekulić, Vladislav | Skinner, Frances K. | Wójcik, Daniel K. | Chintaluri, Chaitanya | Cserpán, Dorottya | Somogyvári, Zoltán | Kim, Jae Kyoung | Kilpatrick, Zachary P. | Bennett, Matthew R. | Josić, Kresimir | Elices, Irene | Arroyo, David | Levi, Rafael | Rodriguez, Francisco B. | Varona, Pablo | Hwang, Eunjin | Kim, Bowon | Han, Hio-Been | Kim, Tae | McKenna, James T. | Brown, Ritchie E. | McCarley, Robert W. | Choi, Jee Hyun | Rankin, James | Popp, Pamela Osborn | Rinzel, John | Tabas, Alejandro | Rupp, André | Balaguer-Ballester, Emili | Maturana, Matias I. | Grayden, David B. | Cloherty, Shaun L. | Kameneva, Tatiana | Ibbotson, Michael R. | Meffin, Hamish | Koren, Veronika | Lochmann, Timm | Dragoi, Valentin | Obermayer, Klaus | Psarrou, Maria | Schilstra, Maria | Davey, Neil | Torben-Nielsen, Benjamin | Steuber, Volker | Ju, Huiwen | Yu, Jiao | Hines, Michael L. | Chen, Liang | Yu, Yuguo | Kim, Jimin | Leahy, Will | Shlizerman, Eli | Birgiolas, Justas | Gerkin, Richard C. | Crook, Sharon M. | Viriyopase, Atthaphon | Memmesheimer, Raoul-Martin | Gielen, Stan | Dabaghian, Yuri | DeVito, Justin | Perotti, Luca | Kim, Anmo J. | Fenk, Lisa M. | Cheng, Cheng | Maimon, Gaby | Zhao, Chang | Widmer, Yves | Sprecher, Simon | Senn, Walter | Halnes, Geir | Mäki-Marttunen, Tuomo | Keller, Daniel | Pettersen, Klas H. | Andreassen, Ole A. | Einevoll, Gaute T. | Yamada, Yasunori | Steyn-Ross, Moira L. | Alistair Steyn-Ross, D. | Mejias, Jorge F. | Murray, John D. | Kennedy, Henry | Wang, Xiao-Jing | Kruscha, Alexandra | Grewe, Jan | Benda, Jan | Lindner, Benjamin | Badel, Laurent | Ohta, Kazumi | Tsuchimoto, Yoshiko | Kazama, Hokto | Kahng, B. | Tam, Nicoladie D. | Pollonini, Luca | Zouridakis, George | Soh, Jaehyun | Kim, DaeEun | Yoo, Minsu | Palmer, S. E. | Culmone, Viviana | Bojak, Ingo | Ferrario, Andrea | Merrison-Hort, Robert | Borisyuk, Roman | Kim, Chang Sub | Tezuka, Taro | Joo, Pangyu | Rho, Young-Ah | Burton, Shawn D. | Bard Ermentrout, G. | Jeong, Jaeseung | Urban, Nathaniel N. | Marsalek, Petr | Kim, Hoon-Hee | Moon, Seok-hyun | Lee, Do-won | Lee, Sung-beom | Lee, Ji-yong | Molkov, Yaroslav I. | Hamade, Khaldoun | Teka, Wondimu | Barnett, William H. | Kim, Taegyo | Markin, Sergey | Rybak, Ilya A. | Forro, Csaba | Dermutz, Harald | Demkó, László | Vörös, János | Babichev, Andrey | Huang, Haiping | Verduzco-Flores, Sergio | Dos Santos, Filipa | Andras, Peter | Metzner, Christoph | Schweikard, Achim | Zurowski, Bartosz | Roach, James P. | Sander, Leonard M. | Zochowski, Michal R. | Skilling, Quinton M. | Ognjanovski, Nicolette | Aton, Sara J. | Zochowski, Michal | Wang, Sheng-Jun | Ouyang, Guang | Guang, Jing | Zhang, Mingsha | Michael Wong, K. Y. | Zhou, Changsong | Robinson, Peter A. | Sanz-Leon, Paula | Drysdale, Peter M. | Fung, Felix | Abeysuriya, Romesh G. | Rennie, Chris J. | Zhao, Xuelong | Choe, Yoonsuck | Yang, Huei-Fang | Mi, Yuanyuan | Lin, Xiaohan | Wu, Si | Liedtke, Joscha | Schottdorf, Manuel | Wolf, Fred | Yamamura, Yoriko | Wickens, Jeffery R. | Rumbell, Timothy | Ramsey, Julia | Reyes, Amy | Draguljić, Danel | Hof, Patrick R. | Luebke, Jennifer | Weaver, Christina M. | He, Hu | Yang, Xu | Ma, Hailin | Xu, Zhiheng | Wang, Yuzhe | Baek, Kwangyeol | Morris, Laurel S. | Kundu, Prantik | Voon, Valerie | Agnes, Everton J. | Vogels, Tim P. | Podlaski, William F. | Giese, Martin | Kuravi, Pradeep | Vogels, Rufin | Seeholzer, Alexander | Podlaski, William | Ranjan, Rajnish | Vogels, Tim | Torres, Joaquin J. | Baroni, Fabiano | Latorre, Roberto | Gips, Bart | Lowet, Eric | Roberts, Mark J. | de Weerd, Peter | Jensen, Ole | van der Eerden, Jan | Goodarzinick, Abdorreza | Niry, Mohammad D. | Valizadeh, Alireza | Pariz, Aref | Parsi, Shervin S. | Warburton, Julia M. | Marucci, Lucia | Tamagnini, Francesco | Brown, Jon | Tsaneva-Atanasova, Krasimira | Kleberg, Florence I. | Triesch, Jochen | Moezzi, Bahar | Iannella, Nicolangelo | Schaworonkow, Natalie | Plogmacher, Lukas | Goldsworthy, Mitchell R. | Hordacre, Brenton | McDonnell, Mark D. | Ridding, Michael C. | Zapotocky, Martin | Smit, Daniel | Fouquet, Coralie | Trembleau, Alain | Dasgupta, Sakyasingha | Nishikawa, Isao | Aihara, Kazuyuki | Toyoizumi, Taro | Robb, Daniel T. | Mellen, Nick | Toporikova, Natalia | Tang, Rongxiang | Tang, Yi-Yuan | Liang, Guangsheng | Kiser, Seth A. | Howard, James H. | Goncharenko, Julia | Voronenko, Sergej O. | Ahamed, Tosif | Stephens, Greg | Yger, Pierre | Lefebvre, Baptiste | Spampinato, Giulia Lia Beatrice | Esposito, Elric | et Olivier Marre, Marcel Stimberg | Choi, Hansol | Song, Min-Ho | Chung, SueYeon | Lee, Dan D. | Sompolinsky, Haim | Phillips, Ryan S. | Smith, Jeffrey | Chatzikalymniou, Alexandra Pierri | Ferguson, Katie | Alex Cayco Gajic, N. | Clopath, Claudia | Angus Silver, R. | Gleeson, Padraig | Marin, Boris | Sadeh, Sadra | Quintana, Adrian | Cantarelli, Matteo | Dura-Bernal, Salvador | Lytton, William W. | Davison, Andrew | Li, Luozheng | Zhang, Wenhao | Wang, Dahui | Song, Youngjo | Park, Sol | Choi, Ilhwan | Shin, Hee-sup | Choi, Hannah | Pasupathy, Anitha | Shea-Brown, Eric | Huh, Dongsung | Sejnowski, Terrence J. | Vogt, Simon M. | Kumar, Arvind | Schmidt, Robert | Van Wert, Stephen | Schiff, Steven J. | Veale, Richard | Scheutz, Matthias | Lee, Sang Wan | Gallinaro, Júlia | Rotter, Stefan | Rubchinsky, Leonid L. | Cheung, Chung Ching | Ratnadurai-Giridharan, Shivakeshavan | Shomali, Safura Rashid | Ahmadabadi, Majid Nili | Shimazaki, Hideaki | Nader Rasuli, S. | Zhao, Xiaochen | Rasch, Malte J. | Wilting, Jens | Priesemann, Viola | Levina, Anna | Rudelt, Lucas | Lizier, Joseph T. | Spinney, Richard E. | Rubinov, Mikail | Wibral, Michael | Bak, Ji Hyun | Pillow, Jonathan | Zaho, Yuan | Park, Il Memming | Kang, Jiyoung | Park, Hae-Jeong | Jang, Jaeson | Paik, Se-Bum | Choi, Woochul | Lee, Changju | Song, Min | Lee, Hyeonsu | Park, Youngjin | Yilmaz, Ergin | Baysal, Veli | Ozer, Mahmut | Saska, Daniel | Nowotny, Thomas | Chan, Ho Ka | Diamond, Alan | Herrmann, Christoph S. | Murray, Micah M. | Ionta, Silvio | Hutt, Axel | Lefebvre, Jérémie | Weidel, Philipp | Duarte, Renato | Morrison, Abigail | Lee, Jung H. | Iyer, Ramakrishnan | Mihalas, Stefan | Koch, Christof | Petrovici, Mihai A. | Leng, Luziwei | Breitwieser, Oliver | Stöckel, David | Bytschok, Ilja | Martel, Roman | Bill, Johannes | Schemmel, Johannes | Meier, Karlheinz | Esler, Timothy B. | Burkitt, Anthony N. | Kerr, Robert R. | Tahayori, Bahman | Nolte, Max | Reimann, Michael W. | Muller, Eilif | Markram, Henry | Parziale, Antonio | Senatore, Rosa | Marcelli, Angelo | Skiker, K. | Maouene, M. | Neymotin, Samuel A. | Seidenstein, Alexandra | Lakatos, Peter | Sanger, Terence D. | Menzies, Rosemary J. | McLauchlan, Campbell | van Albada, Sacha J. | Kedziora, David J. | Neymotin, Samuel | Kerr, Cliff C. | Suter, Benjamin A. | Shepherd, Gordon M. G. | Ryu, Juhyoung | Lee, Sang-Hun | Lee, Joonwon | Lee, Hyang Jung | Lim, Daeseob | Wang, Jisung | Lee, Heonsoo | Jung, Nam | Anh Quang, Le | Maeng, Seung Eun | Lee, Tae Ho | Lee, Jae Woo | Park, Chang-hyun | Ahn, Sora | Moon, Jangsup | Choi, Yun Seo | Kim, Juhee | Jun, Sang Beom | Lee, Seungjun | Lee, Hyang Woon | Jo, Sumin | Jun, Eunji | Yu, Suin | Goetze, Felix | Lai, Pik-Yin | Kim, Seonghyun | Kwag, Jeehyun | Jang, Hyun Jae | Filipović, Marko | Reig, Ramon | Aertsen, Ad | Silberberg, Gilad | Bachmann, Claudia | Buttler, Simone | Jacobs, Heidi | Dillen, Kim | Fink, Gereon R. | Kukolja, Juraj | Kepple, Daniel | Giaffar, Hamza | Rinberg, Dima | Shea, Steven | Koulakov, Alex | Bahuguna, Jyotika | Tetzlaff, Tom | Kotaleski, Jeanette Hellgren | Kunze, Tim | Peterson, Andre | Knösche, Thomas | Kim, Minjung | Kim, Hojeong | Park, Ji Sung | Yeon, Ji Won | Kim, Sung-Phil | Kang, Jae-Hwan | Lee, Chungho | Spiegler, Andreas | Petkoski, Spase | Palva, Matias J. | Jirsa, Viktor K. | Saggio, Maria L. | Siep, Silvan F. | Stacey, William C. | Bernar, Christophe | Choung, Oh-hyeon | Jeong, Yong | Lee, Yong-il | Kim, Su Hyun | Jeong, Mir | Lee, Jeungmin | Kwon, Jaehyung | Kralik, Jerald D. | Jahng, Jaehwan | Hwang, Dong-Uk | Kwon, Jae-Hyung | Park, Sang-Min | Kim, Seongkyun | Kim, Hyoungkyu | Kim, Pyeong Soo | Yoon, Sangsup | Lim, Sewoong | Park, Choongseok | Miller, Thomas | Clements, Katie | Ahn, Sungwoo | Ji, Eoon Hye | Issa, Fadi A. | Baek, JeongHun | Oba, Shigeyuki | Yoshimoto, Junichiro | Doya, Kenji | Ishii, Shin | Mosqueiro, Thiago S. | Strube-Bloss, Martin F. | Smith, Brian | Huerta, Ramon | Hadrava, Michal | Hlinka, Jaroslav | Bos, Hannah | Helias, Moritz | Welzig, Charles M. | Harper, Zachary J. | Kim, Won Sup | Shin, In-Seob | Baek, Hyeon-Man | Han, Seung Kee | Richter, René | Vitay, Julien | Beuth, Frederick | Hamker, Fred H. | Toppin, Kelly | Guo, Yixin | Graham, Bruce P. | Kale, Penelope J. | Gollo, Leonardo L. | Stern, Merav | Abbott, L. F. | Fedorov, Leonid A. | Giese, Martin A. | Ardestani, Mohammad Hovaidi | Faraji, Mohammad Javad | Preuschoff, Kerstin | Gerstner, Wulfram | van Gendt, Margriet J. | Briaire, Jeroen J. | Kalkman, Randy K. | Frijns, Johan H. M. | Lee, Won Hee | Frangou, Sophia | Fulcher, Ben D. | Tran, Patricia H. P. | Fornito, Alex | Gliske, Stephen V. | Lim, Eugene | Holman, Katherine A. | Fink, Christian G. | Kim, Jinseop S. | Mu, Shang | Briggman, Kevin L. | Sebastian Seung, H. | Wegener, Detlef | Bohnenkamp, Lisa | Ernst, Udo A. | Devor, Anna | Dale, Anders M. | Lines, Glenn T. | Edwards, Andy | Tveito, Aslak | Hagen, Espen | Senk, Johanna | Diesmann, Markus | Schmidt, Maximilian | Bakker, Rembrandt | Shen, Kelly | Bezgin, Gleb | Hilgetag, Claus-Christian | van Albada, Sacha Jennifer | Sun, Haoqi | Sourina, Olga | Huang, Guang-Bin | Klanner, Felix | Denk, Cornelia | Glomb, Katharina | Ponce-Alvarez, Adrián | Gilson, Matthieu | Ritter, Petra | Deco, Gustavo | Witek, Maria A. G. | Clarke, Eric F. | Hansen, Mads | Wallentin, Mikkel | Kringelbach, Morten L. | Vuust, Peter | Klingbeil, Guido | De Schutter, Erik | Chen, Weiliang | Zang, Yunliang | Hong, Sungho | Takashima, Akira | Zamora, Criseida | Gallimore, Andrew R. | Goldschmidt, Dennis | Manoonpong, Poramate | Karoly, Philippa J. | Freestone, Dean R. | Soundry, Daniel | Kuhlmann, Levin | Paninski, Liam | Cook, Mark | Lee, Jaejin | Fishman, Yonatan I. | Cohen, Yale E. | Roberts, James A. | Cocchi, Luca | Sweeney, Yann | Lee, Soohyun | Jung, Woo-Sung | Kim, Youngsoo | Jung, Younginha | Song, Yoon-Kyu | Chavane, Frédéric | Soman, Karthik | Muralidharan, Vignesh | Srinivasa Chakravarthy, V. | Shivkumar, Sabyasachi | Mandali, Alekhya | Pragathi Priyadharsini, B. | Mehta, Hima | Davey, Catherine E. | Brinkman, Braden A. W. | Kekona, Tyler | Rieke, Fred | Buice, Michael | De Pittà, Maurizio | Berry, Hugues | Brunel, Nicolas | Breakspear, Michael | Marsat, Gary | Drew, Jordan | Chapman, Phillip D. | Daly, Kevin C. | Bradle, Samual P. | Seo, Sat Byul | Su, Jianzhong | Kavalali, Ege T. | Blackwell, Justin | Shiau, LieJune | Buhry, Laure | Basnayake, Kanishka | Lee, Sue-Hyun | Levy, Brandon A. | Baker, Chris I. | Leleu, Timothée | Philips, Ryan T. | Chhabria, Karishma
BMC Neuroscience  2016;17(Suppl 1):54.
Table of contents
A1 Functional advantages of cell-type heterogeneity in neural circuits
Tatyana O. Sharpee
A2 Mesoscopic modeling of propagating waves in visual cortex
Alain Destexhe
A3 Dynamics and biomarkers of mental disorders
Mitsuo Kawato
F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons
Vladislav Sekulić, Frances K. Skinner
F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains
Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári
F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks.
Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić
O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators
Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona
O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain
Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi
O3 Modeling auditory stream segregation, build-up and bistability
James Rankin, Pamela Osborn Popp, John Rinzel
O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields
Alejandro Tabas, André Rupp, Emili Balaguer-Ballester
O5 A simple model of retinal response to multi-electrode stimulation
Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin
O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task
Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer
O7 Input-location dependent gain modulation in cerebellar nucleus neurons
Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber
O8 Analytic solution of cable energy function for cortical axons and dendrites
Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu
O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network
Jimin Kim, Will Leahy, Eli Shlizerman
O10 Is the model any good? Objective criteria for computational neuroscience model selection
Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook
O11 Cooperation and competition of gamma oscillation mechanisms
Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen
O12 A discrete structure of the brain waves
Yuri Dabaghian, Justin DeVito, Luca Perotti
O13 Direction-specific silencing of the Drosophila gaze stabilization system
Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon
O14 What does the fruit fly think about values? A model of olfactory associative learning
Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn
O15 Effects of ionic diffusion on power spectra of local field potentials (LFP)
Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll
O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits
Yasunori Yamada
O17 Spatial coarse-graining the brain: origin of minicolumns
Moira L. Steyn-Ross, D. Alistair Steyn-Ross
O18 Modeling large-scale cortical networks with laminar structure
Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang
O19 Information filtering by partial synchronous spikes in a neural population
Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner
O20 Decoding context-dependent olfactory valence in Drosophila
Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama
P1 Neural network as a scale-free network: the role of a hub
B. Kahng
P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging
Nicoladie D. Tam
P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique
Nicoladie D.Tam, Luca Pollonini, George Zouridakis
P4 Modeling jamming avoidance of weakly electric fish
Jaehyun Soh, DaeEun Kim
P5 Synergy and redundancy of retinal ganglion cells in prediction
Minsu Yoo, S. E. Palmer
P6 A neural field model with a third dimension representing cortical depth
Viviana Culmone, Ingo Bojak
P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord
Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk
P8 The recognition dynamics in the brain
Chang Sub Kim
P9 Multivariate spike train analysis using a positive definite kernel
Taro Tezuka
P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia
Pangyu Joo
P11 The ionic basis of heterogeneity affects stochastic synchrony
Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban
P12 Circular statistics of noise in spike trains with a periodic component
Petr Marsalek
P14 Representations of directions in EEG-BCI using Gaussian readouts
Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong
P15 Action selection and reinforcement learning in basal ganglia during reaching movements
Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak
P17 Axon guidance: modeling axonal growth in T-Junction assay
Csaba Forro, Harald Dermutz, László Demkó, János Vörös
P19 Transient cell assembly networks encode persistent spatial memories
Yuri Dabaghian, Andrey Babichev
P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons
Haiping Huang
P21 Design of biologically-realistic simulations for motor control
Sergio Verduzco-Flores
P22 Towards understanding the functional impact of the behavioural variability of neurons
Filipa Dos Santos, Peter Andras
P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia
Christoph Metzner, Achim Schweikard, Bartosz Zurowski
P24 Memory recall and spike frequency adaptation
James P. Roach, Leonard M. Sander, Michal R. Zochowski
P25 Stability of neural networks and memory consolidation preferentially occur near criticality
Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski
P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems
Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou
P27 Neurofield: a C++ library for fast simulation of 2D neural field models
Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao
P28 Action-based grounding: Beyond encoding/decoding in neural code
Yoonsuck Choe, Huei-Fang Yang
P29 Neural computation in a dynamical system with multiple time scales
Yuanyuan Mi, Xiaohan Lin, Si Wu
P30 Maximum entropy models for 3D layouts of orientation selectivity
Joscha Liedtke, Manuel Schottdorf, Fred Wolf
P31 A behavioral assay for probing computations underlying curiosity in rodents
Yoriko Yamamura, Jeffery R. Wickens
P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models
Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver
P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm
Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang
P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis
Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon
P35 Dynamics of cooperative excitatory and inhibitory plasticity
Everton J. Agnes, Tim P. Vogels
P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons
William F. Podlaski, Tim P. Vogels
P37 Phenomenological neural model for adaptation of neurons in area IT
Martin Giese, Pradeep Kuravi, Rufin Vogels
P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment
Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels
P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations
Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona
P40 Different roles for transient and sustained activity during active visual processing
Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden
P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications
Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh
P42 High frequency neuron can facilitate propagation of signal in neural networks
Aref Pariz, Shervin S. Parsi, Alireza Valizadeh
P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus
Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova
P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP
Florence I. Kleberg, Jochen Triesch
P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex
Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. Ridding, Jochen Triesch
P46 Structure and dynamics of axon network formed in primary cell culture
Martin Zapotocky, Daniel Smit, Coralie Fouquet, Alain Trembleau
P47 Efficient signal processing and sampling in random networks that generate variability
Sakyasingha Dasgupta, Isao Nishikawa, Kazuyuki Aihara, Taro Toyoizumi
P48 Modeling the effect of riluzole on bursting in respiratory neural networks
Daniel T. Robb, Nick Mellen, Natalia Toporikova
P49 Mapping relaxation training using effective connectivity analysis
Rongxiang Tang, Yi-Yuan Tang
P50 Modeling neuron oscillation of implicit sequence learning
Guangsheng Liang, Seth A. Kiser, James H. Howard, Jr., Yi-Yuan Tang
P51 The role of cerebellar short-term synaptic plasticity in the pathology and medication of downbeat nystagmus
Julia Goncharenko, Neil Davey, Maria Schilstra, Volker Steuber
P52 Nonlinear response of noisy neurons
Sergej O. Voronenko, Benjamin Lindner
P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion
Tosif Ahamed, Greg Stephens
P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings
Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre
P55 Sufficient sampling rates for fast hand motion tracking
Hansol Choi, Min-Ho Song
P56 Linear readout of object manifolds
SueYeon Chung, Dan D. Lee, Haim Sompolinsky
P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves
Ryan S. Phillips, Jeffrey Smith
P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus
Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner
P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning
N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver
P60 A set of curated cortical models at multiple scales on Open Source Brain
Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver
P61 A synaptic story of dynamical information encoding in neural adaptation
Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu
P62 Physical modeling of rule-observant rodent behavior
Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin
P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes
Hannah Choi, Anitha Pasupathy, Eric Shea-Brown
P65 Stability of FORCE learning on spiking and rate-based networks
Dongsung Huh, Terrence J. Sejnowski
P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments
Simon M. Vogt, Arvind Kumar, Robert Schmidt
P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation
Stephen Van Wert, Steven J. Schiff
P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo
Richard Veale, Matthias Scheutz
P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions
Sang Wan Lee
P70 Maturation of sensory networks through homeostatic structural plasticity
Júlia Gallinaro, Stefan Rotter
P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings
Paula Sanz-Leon, Peter A. Robinson
P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study
Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan
P73 Exact spike-timing distribution reveals higher-order interactions of neurons
Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli
P74 Neural mechanism of visual perceptual learning using a multi-layered neural network
Xiaochen Zhao, Malte J. Rasch
P75 Inferring collective spiking dynamics from mostly unobserved systems
Jens Wilting, Viola Priesemann
P76 How to infer distributions in the brain from subsampled observations
Anna Levina, Viola Priesemann
P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons
Lucas Rudelt, Joseph T. Lizier, Viola Priesemann
P78 A nearest-neighbours based estimator for transfer entropy between spike trains
Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann
P79 Active learning of psychometric functions with multinomial logistic models
Ji Hyun Bak, Jonathan Pillow
P81 Inferring low-dimensional network dynamics with variational latent Gaussian process
Yuan Zaho, Il Memming Park
P82 Computational investigation of energy landscapes in the resting state subcortical brain network
Jiyoung Kang, Hae-Jeong Park
P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map
Jaeson Jang, Se-Bum Paik
P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition
Woochul Choi, Se-Bum Paik
P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map
Changju Lee, Jaeson Jang, Se-Bum Paik
P86 Computational method classifying neural network activity patterns for imaging data
Min Song, Hyeonsu Lee, Se-Bum Paik
P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory
Youngjin Park, Woochul Choi, Se-Bum Paik
P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons
Ergin Yilmaz, Veli Baysal, Mahmut Ozer
P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance
Veronika Koren, Klaus Obermayer
P90 Methods for building accurate models of individual neurons
Daniel Saska, Thomas Nowotny
P91 A full size mathematical model of the early olfactory system of honeybees
Ho Ka Chan, Alan Diamond, Thomas Nowotny
P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks
Christoph S. Herrmann, Micah M. Murray, Silvio Ionta, Axel Hutt, Jérémie Lefebvre
P93 Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input
Philipp Weidel, Renato Duarte, Abigail Morrison
P94 Modulation of tuning induced by abrupt reduction of SST cell activity
Jung H. Lee, Ramakrishnan Iyer, Stefan Mihalas
P95 The functional role of VIP cell activation during locomotion
Jung H. Lee, Ramakrishnan Iyer, Christof Koch, Stefan Mihalas
P96 Stochastic inference with spiking neural networks
Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier
P97 Modeling orientation-selective electrical stimulation with retinal prostheses
Timothy B. Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin
P98 Ion channel noise can explain firing correlation in auditory nerves
Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell
P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit
Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram
P100 On the representation of arm reaching movements: a computational model
Antonio Parziale, Rosa Senatore, Angelo Marcelli
P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior
Rosa Senatore, Antonio Parziale, Angelo Marcelli
P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge
K. Skiker, M. Maouene
P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia
Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton
P104 Effect of network size on computational capacity
Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr
P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks
Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton
P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas
Juhyoung Ryu, Sang-Hun Lee
P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception
Joonwon Lee, Sang-Hun Lee
P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making
Hyang Jung Lee, Sang-Hun Lee
P110 A Bayesian algorithm for phoneme Perception and its neural implementation
Daeseob Lim, Sang-Hun Lee
P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol
Jisung Wang, Heonsoo Lee
P112 Self-organized criticality of neural avalanche in a neural model on complex networks
Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee
P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model
Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee
P114 Computational model to replicate seizure suppression effect by electrical stimulation
Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee
P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy
Felix Goetze, Pik-Yin Lai
P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell
Seonghyun Kim, Jeehyun Kwag
P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model
Hyun Jae Jang, Jeehyun Kwag
P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum
Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar
P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease
Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison
P120 Learning sparse representations in the olfactory bulb
Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov
P121 Functional classification of homologous basal-ganglia networks
Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski
P122 Short term memory based on multistability
Tim Kunze, Andre Peterson, Thomas Knösche
P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units
Minjung Kim, Hojeong Kim
P125 Decoding laser-induced somatosensory information from EEG
Ji Sung Park, Ji Won Yeon, Sung-Phil Kim
P126 Phase synchronization of alpha activity for EEG-based personal authentication
Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim
P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model
Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa
P130 Epileptic seizures in the unfolding of a codimension-3 singularity
Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa
P131 Incremental dimensional exploratory reasoning under multi-dimensional environment
Oh-hyeon Choung, Yong Jeong
P132 A low-cost model of eye movements and memory in personal visual cognition
Yong-il Lee, Jaeseung Jeong
P133 Complex network analysis of structural connectome of autism spectrum disorder patients
Su Hyun Kim, Mir Jeong, Jaeseung Jeong
P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip
Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong
P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making
Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong
P136 Detecting purchase decision based on hyperfrontality of the EEG
Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong
P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome
Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong
P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans
Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong
P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it?
Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong
P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish
Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa
P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data
JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii
P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems
Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta
P146 Swinging networks
Michal Hadrava, Jaroslav Hlinka
P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions
Hannah Bos, Moritz Helias
P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning
Charles M. Welzig, Zachary J. Harper
P149 Multiscale complexity analysis for the segmentation of MRI images
Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han
P150 A neuro-computational model of emotional attention
René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker
P151 Multi-site delayed feedback stimulation in parkinsonian networks
Kelly Toppin, Yixin Guo
P152 Bistability in Hodgkin–Huxley-type equations
Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden
P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency
Mark D. McDonnell, Bruce P. Graham
P154 Quantifying resilience patterns in brain networks: the importance of directionality
Penelope J. Kale, Leonardo L. Gollo
P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations
Merav Stern, L. F. Abbott
P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues
Leonid A. Fedorov, Martin A. Giese
P157 Spiking model for the interaction between action recognition and action execution
Mohammad Hovaidi Ardestani, Martin Giese
P158 Surprise-modulated belief update: how to learn within changing environments?
Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner
P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation
Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns
P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks
Won Hee Lee, Sophia Frangou
P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis
Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito
P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations
Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink
P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse
Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers
P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes
Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst
P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology
Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll
P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach
Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. Andreassen
P167 Local field potentials in a 4 × 4 mm2 multi-layered network model
Espen Hagen, Johanna Senk, Sacha J. van Albada, Markus Diesmann
P168 A spiking network model explains multi-scale properties of cortical dynamics
Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Claus-Christian Hilgetag, Markus Diesmann, Sacha Jennifer van Albada
P169 Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups
Haoqi Sun, Olga Sourina, Guang-Bin Huang, Felix Klanner, Cornelia Denk
P170 Tensor decomposition reveals RSNs in simulated resting state fMRI
Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco
P171 Getting in the groove: testing a new model-based method for comparing task-evoked vs resting-state activity in fMRI data on music listening
Matthieu Gilson, Maria AG Witek, Eric F. Clarke, Mads Hansen, Mikkel Wallentin, Gustavo Deco, Morten L. Kringelbach, Peter Vuust
P172 STochastic engine for pathway simulation (STEPS) on massively parallel processors
Guido Klingbeil, Erik De Schutter
P173 Toolkit support for complex parallel spatial stochastic reaction–diffusion simulation in STEPS
Weiliang Chen, Erik De Schutter
P174 Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input
Yunliang Zang, Erik De Schutter
P175 Dendritic morphology determines how dendrites are organized into functional subunits
Sungho Hong, Akira Takashima, Erik De Schutter
P176 A model of Ca2+/calmodulin-dependent protein kinase II activity in long term depression at Purkinje cells
Criseida Zamora, Andrew R. Gallimore, Erik De Schutter
P177 Reward-modulated learning of population-encoded vectors for insect-like navigation in embodied agents
Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta
P178 Data-driven neural models part II: connectivity patterns of human seizures
Philippa J. Karoly, Dean R. Freestone, Daniel Soundry, Levin Kuhlmann, Liam Paninski, Mark Cook
P179 Data-driven neural models part I: state and parameter estimation
Dean R. Freestone, Philippa J. Karoly, Daniel Soundry, Levin Kuhlmann, Mark Cook
P180 Spectral and spatial information processing in human auditory streaming
Jaejin Lee, Yonatan I. Fishman, Yale E. Cohen
P181 A tuning curve for the global effects of local perturbations in neural activity: Mapping the systems-level susceptibility of the brain
Leonardo L. Gollo, James A. Roberts, Luca Cocchi
P182 Diverse homeostatic responses to visual deprivation mediated by neural ensembles
Yann Sweeney, Claudia Clopath
P183 Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography
Soohyun Lee, Woo-Sung Jung, Jee Hyun Choi
P184 Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep
Bowon Kim, Youngsoo Kim, Eunjin Hwang, Jee Hyun Choi
P185 Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response
Younginha Jung, Eunjin Hwang, Yoon-Kyu Song, Jee Hyun Choi
P186 Neural field model of localized orientation selective activation in V1
James Rankin, Frédéric Chavane
P187 An oscillatory network model of Head direction and Grid cells using locomotor inputs
Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P188 A computational model of hippocampus inspired by the functional architecture of basal ganglia
Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P189 A computational architecture to model the microanatomy of the striatum and its functional properties
Sabyasachi Shivkumar, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P190 A scalable cortico-basal ganglia model to understand the neural dynamics of targeted reaching
Vignesh Muralidharan, Alekhya Mandali, B. Pragathi Priyadharsini, Hima Mehta, V. Srinivasa Chakravarthy
P191 Emergence of radial orientation selectivity from synaptic plasticity
Catherine E. Davey, David B. Grayden, Anthony N. Burkitt
P192 How do hidden units shape effective connections between neurons?
Braden A. W. Brinkman, Tyler Kekona, Fred Rieke, Eric Shea-Brown, Michael Buice
P193 Characterization of neural firing in the presence of astrocyte-synapse signaling
Maurizio De Pittà, Hugues Berry, Nicolas Brunel
P194 Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics
James A. Roberts, Leonardo L. Gollo, Michael Breakspear
P195 Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings
Gary Marsat, Jordan Drew, Phillip D. Chapman, Kevin C. Daly, Samual P. Bradley
P196 Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses
Sat Byul Seo, Jianzhong Su, Ege T. Kavalali, Justin Blackwell
P199 Gamma oscillation via adaptive exponential integrate-and-fire neurons
LieJune Shiau, Laure Buhry, Kanishka Basnayake
P200 Visual face representations during memory retrieval compared to perception
Sue-Hyun Lee, Brandon A. Levy, Chris I. Baker
P201 Top-down modulation of sequential activity within packets modeled using avalanche dynamics
Timothée Leleu, Kazuyuki Aihara
Q28 An auto-encoder network realizes sparse features under the influence of desynchronized vascular dynamics
Ryan T. Philips, Karishma Chhabria, V. Srinivasa Chakravarthy
doi:10.1186/s12868-016-0283-6
PMCID: PMC5001212  PMID: 27534393
25.  Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles 
Genetics  2016;203(3):1425-1438.
Whole-genome multiomic profiles hold valuable information for the analysis and prediction of disease risk and progression. However, integrating high-dimensional multilayer omic data into risk-assessment models is statistically and computationally challenging. We describe a statistical framework, the Bayesian generalized additive model ((BGAM), and present software for integrating multilayer high-dimensional inputs into risk-assessment models. We used BGAM and data from The Cancer Genome Atlas for the analysis and prediction of survival after diagnosis of breast cancer. We developed a sequence of studies to (1) compare predictions based on single omics with those based on clinical covariates commonly used for the assessment of breast cancer patients (COV), (2) evaluate the benefits of combining COV and omics, (3) compare models based on (a) COV and gene expression profiles from oncogenes with (b) COV and whole-genome gene expression (WGGE) profiles, and (4) evaluate the impacts of combining multiple omics and their interactions. We report that (1) WGGE profiles and whole-genome methylation (METH) profiles offer more predictive power than any of the COV commonly used in clinical practice (e.g., subtype and stage), (2) adding WGGE or METH profiles to COV increases prediction accuracy, (3) the predictive power of WGGE profiles is considerably higher than that based on expression from large-effect oncogenes, and (4) the gain in prediction accuracy when combining multiple omics is consistent. Our results show the feasibility of omic integration and highlight the importance of WGGE and METH profiles in breast cancer, achieving gains of up to 7 points area under the curve (AUC) over the COV in some cases.
doi:10.1534/genetics.115.185181
PMCID: PMC4937492  PMID: 27129736
prediction of complex traits; diseases risk; omics integration; GenPred; Shared data resource; genomic selection

Results 1-25 (2349894)