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1.  TEMPI: probabilistic modeling time-evolving differential PPI networks with multiPle information 
Bioinformatics  2014;30(17):i453-i460.
Motivation: Time-evolving differential protein–protein interaction (PPI) networks are essential to understand serial activation of differentially regulated (up- or downregulated) cellular processes (DRPs) and their interplays over time. Despite developments in the network inference, current methods are still limited in identifying temporal transition of structures of PPI networks, DRPs associated with the structural transition and the interplays among the DRPs over time.
Results: Here, we present a probabilistic model for estimating Time-Evolving differential PPI networks with MultiPle Information (TEMPI). This model describes probabilistic relationships among network structures, time-course gene expression data and Gene Ontology biological processes (GOBPs). By maximizing the likelihood of the probabilistic model, TEMPI estimates jointly the time-evolving differential PPI networks (TDNs) describing temporal transition of PPI network structures together with serial activation of DRPs associated with transiting networks. This joint estimation enables us to interpret the TDNs in terms of temporal transition of the DRPs. To demonstrate the utility of TEMPI, we applied it to two time-course datasets. TEMPI identified the TDNs that correctly delineated temporal transition of DRPs and time-dependent associations between the DRPs. These TDNs provide hypotheses for mechanisms underlying serial activation of key DRPs and their temporal associations.
Availability and implementation: Source code and sample data files are available at
Contact: or
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC4147907  PMID: 25161233
2.  Principal network analysis: identification of subnetworks representing major dynamics using gene expression data 
Bioinformatics  2010;27(3):391-398.
Motivation: Systems biology attempts to describe complex systems behaviors in terms of dynamic operations of biological networks. However, there is lack of tools that can effectively decode complex network dynamics over multiple conditions.
Results: We present principal network analysis (PNA) that can automatically capture major dynamic activation patterns over multiple conditions and then generate protein and metabolic subnetworks for the captured patterns. We first demonstrated the utility of this method by applying it to a synthetic dataset. The results showed that PNA correctly captured the subnetworks representing dynamics in the data. We further applied PNA to two time-course gene expression profiles collected from (i) MCF7 cells after treatments of HRG at multiple doses and (ii) brain samples of four strains of mice infected with two prion strains. The resulting subnetworks and their interactions revealed network dynamics associated with HRG dose-dependent regulation of cell proliferation and differentiation and early PrPSc accumulation during prion infection.
Availability: The web-based software is available at:
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3031040  PMID: 21193522
3.  Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices 
Nucleic Acids Research  2011;40(5):e38.
Accurate prediction of transcription factor binding sites (TFBSs) is a prerequisite for identifying cis-regulatory modules that underlie transcriptional regulatory circuits encoded in the genome. Here, we present a computational framework for detecting TFBSs, when multiple position weight matrices (PWMs) for a transcription factor are available. Grouping multiple PWMs of a transcription factor (TF) based on their sequence similarity improves the specificity of TFBS prediction, which was evaluated using multiple genome-wide ChIP-Seq data sets from 26 TFs. The Z-scores of the area under a receiver operating characteristic curve (AUC) values of 368 TFs were calculated and used to statistically identify co-occurring regulatory motifs in the TF bound ChIP loci. Motifs that are co-occurring along with the empirical bindings of E2F, JUN or MYC have been evaluated, in the basal or stimulated condition. Results prove our method can be useful to systematically identify the co-occurring motifs of the TF for the given conditions.
PMCID: PMC3300004  PMID: 22187154
4.  Odor-Dependent Hemodynamic Responses Measured with NIRS in the Main Olfactory Bulb of Anesthetized Rats 
Experimental Neurobiology  2011;20(4):189-196.
In this study, we characterize the hemodynamic changes in the main olfactory bulb of anesthetized Sprague-Dawley (SD) rats with near-infrared spectroscopy (NIRS, ISS Imagent) during presentation of two different odorants. Odorants were presented for 10 seconds with clean air via an automatic odor stimulator. Odorants are: (i) plain air as a reference (Blank), (ii) 2-Heptanone (HEP), (iii) Isopropylbenzene (IB). Our results indicated that a plain air did not cause any change in the concentrations of oxygenated (Δ[HbO2]) and deoxygenated hemoglobin (Δ[Hbr]), but HEP and IB induced strong changes. Furthermore, these odor-specific changes had regional differences within the MOB. Our results suggest that NIRS technology might be a useful tool to identify of various odorants in a non-invasive manner using animals which has a superb olfactory system.
PMCID: PMC3268153  PMID: 22355264
near-infrared spectroscopy (NIRS); hemodynamic response; main olfactory bulb (MOB); non-invasive; odorant; brain-machine interface (BMI)
5.  Prediction and Experimental Validation of Novel STAT3 Target Genes in Human Cancer Cells 
PLoS ONE  2009;4(9):e6911.
The comprehensive identification of functional transcription factor binding sites (TFBSs) is an important step in understanding complex transcriptional regulatory networks. This study presents a motif-based comparative approach, STAT-Finder, for identifying functional DNA binding sites of STAT3 transcription factor. STAT-Finder combines STAT-Scanner, which was designed to predict functional STAT TFBSs with improved sensitivity, and a motif-based alignment to minimize false positive prediction rates. Using two reference sets containing promoter sequences of known STAT3 target genes, STAT-Finder identified functional STAT3 TFBSs with enhanced prediction efficiency and sensitivity relative to other conventional TFBS prediction tools. In addition, STAT-Finder identified novel STAT3 target genes among a group of genes that are over-expressed in human cancer cells. The binding of STAT3 to the predicted TFBSs was also experimentally confirmed through chromatin immunoprecipitation. Our proposed method provides a systematic approach to the prediction of functional TFBSs that can be applied to other TFs.
PMCID: PMC2731854  PMID: 19730699

Results 1-5 (5)