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2.  Maternal Baicalin Treatment Increases Fetal Lung Surfactant Phospholipids in Rats 
Baicalin is a flavonoid compound purified from the medicinal plant Scutellaria baicalensis Georgi and has been reported to stimulate surfactant protein (SP)-A gene expression in human lung epithelial cell lines (H441). The aims of this study were to determine whether maternal baicalin treatment could increase lung surfactant production and induce lung maturation in fetal rats. This study was performed with timed pregnant Sprague-Dawley rats. One-day baicalin group mothers were injected intraperitoneally with baicalin (5 mg/kg/day) on Day 18 of gestation. Two-day baicalin group mothers were injected intraperitoneally with baicalin (5 mg/kg/day) on Days 17 and 18 of gestation. Control group mothers were injected with vehicle alone on Day 18 of gestation. On Day 19 of gestation, fetuses were delivered by cesarean section. Maternal treatment with 2-day baicalin significantly increased saturated phospholipid when compared with control group and total phospholipid in fetal lung tissue when compared with control and 1-day baicalin groups. Antenatal treatment with 2-day baicalin significantly increased maternal growth hormone when compared with control group. Fetal lung SP-A mRNA expression and maternal serum corticosterone levels were comparable among the three experimental groups. Maternal baicalin treatment increases pulmonary surfactant phospholipids of fetal rat lungs and the improvement was associated with increased maternal serum growth hormone. These results suggest that antenatal baicalin treatment might accelerate fetal rat lung maturation.
PMCID: PMC3135634  PMID: 19584080
3.  Coronary Arteries Segmentation Based on the 3D Discrete Wavelet Transform and 3D Neutrosophic Transform 
BioMed Research International  2015;2015:798303.
Purpose. Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. Methods. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Results. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Conclusion. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.
PMCID: PMC4310241  PMID: 25648181
4.  Ibuprofen Protects Ventilator-Induced Lung Injury by Downregulating Rho-Kinase Activity in Rats 
BioMed Research International  2014;2014:749097.
Background. Ventilator-induced lung injury-(VILI-) induced endothelial permeability is regulated through the Rho-dependent signaling pathway. Ibuprofen inhibits Rho activation in animal models of spinal-cord injury and Alzheimer's disease. The study aims to investigate ibuprofen effects on high tidal volume associated VILI. Methods. Twenty-eight adult male Sprague-Dawley rats were randomized to receive a ventilation strategy with three different interventions for 2 h: (1) a high-volume zero-positive end-expiratory pressure (PEEP) (HVZP) group; (2) an HVZP + ibuprofen 15 mg/kg group; and (3) an HVZP + ibuprofen 30 mg/kg group. A fourth group without ventilation served as the control group. Rho-kinase activity was determined by ratio of phosphorylated ezrin, radixin, and moesin (p-ERM), substrates of Rho-kinase, to total ERM. VILI was characterized by increased pulmonary protein leak, wet-to-dry weight ratio, cytokines level, and Rho guanine nucleotide exchange factor (GEF-H1), RhoA activity, p-ERM/total ERM, and p-myosin light chain (MLC) protein expression. Results. Ibuprofen pretreatment significantly reduced the HVZP ventilation-induced increase in pulmonary protein leak, wet-to-dry weight ratio, bronchoalveolar lavage fluid interleukin-6 and RANTES levels, and lung GEF-H1, RhoA activity, p-ERM/total ERM, and p-MLC protein expression. Conclusion. Ibuprofen attenuated high tidal volume induced pulmonary endothelial hyperpermeability. This protective effect was associated with a reduced Rho-kinase activity.
PMCID: PMC4075182  PMID: 25019086
5.  Cytotoxic Effect of Recombinant Mycobacterium tuberculosis CFP-10/ESAT-6 Protein on the Crucial Pathways of WI-38 Cells 
To unravel the cytotoxic effect of the recombinant CFP-10/ESAT-6 protein (rCFES) on WI-38 cells, an integrative analysis approach, combining time-course microarray data and annotated pathway databases, was proposed with the emphasis on identifying the potentially crucial pathways. The potentially crucial pathways were selected based on a composite criterion characterizing the average significance and topological properties of important genes. The analysis results suggested that the regulatory effect of rCFES was at least involved in cell proliferation, cell motility, cell survival, and metabolisms of WI-38 cells. The survivability of WI-38 cells, in particular, was significantly decreased to 62% with 12.5 μM rCFES. Furthermore, the focal adhesion pathway was identified as the potentially most-crucial pathway and 58 of 65 important genes in this pathway were downregulated by rCFES treatment. Using qRT-PCR, we have confirmed the changes in the expression levels of LAMA4, PIK3R3, BIRC3, and NFKBIA, suggesting that these proteins may play an essential role in the cytotoxic process in the rCFES-treated WI-38 cells.
PMCID: PMC2702506  PMID: 19584916
6.  Pathway-based gene signatures predicting clinical outcome of lung adenocarcinoma 
Scientific Reports  2015;5:10979.
Lung adenocarcinoma is often diagnosed at an advanced stage with poor prognosis. Patients with different clinical outcomes may have similar clinico-pathological characteristics. The results of previous studies for biomarkers for lung adenocarcinoma have generally been inconsistent and limited in clinical application. In this study, we used inverse-variance weighting to combine the hazard ratios for the four datasets and performed pathway analysis to identify prognosis-associated gene signatures. A total of 2,418 genes were found to be significantly associated with overall survival. Of these, a 21-gene signature in the HMGB1/RAGE signalling pathway and a 31-gene signature in the clathrin-coated vesicle cycle pathway were significantly associated with prognosis of lung adenocarcinoma across all four datasets (all p-values < 0.05, log-rank test). We combined the scores for the three pathways to derive a combined pathway-based risk (CPBR) score. Three pathway-based signatures and CPBR score also had more predictive power than single genes. Finally, the CPBR score was validated in two independent cohorts (GSE14814 and GSE13213 in the GEO database) and had significant adjusted hazard ratios 2.72 (p-value < 0.0001) and 1.71 (p-value < 0.0001), respectively. These results could provide a more complete picture of the lung cancer pathogenesis.
PMCID: PMC4455286  PMID: 26042604
7.  Anatomy packing with hierarchical segments: an algorithm for segmentation of pulmonary nodules in CT images 
This paper proposes a semantic segmentation algorithm that provides the spatial distribution patterns of pulmonary ground-glass nodules with solid portions in computed tomography (CT) images.
The proposed segmentation algorithm, anatomy packing with hierarchical segments (APHS), performs pulmonary nodule segmentation and quantification in CT images. In particular, the APHS algorithm consists of two essential processes: hierarchical segmentation tree construction and anatomy packing. It constructs the hierarchical segmentation tree based on region attributes and local contour cues along the region boundaries. Each node of the tree corresponds to the soft boundary associated with a family of nested segmentations through different scales applied by a hierarchical segmentation operator that is used to decompose the image in a structurally coherent manner. The anatomy packing process detects and localizes individual object instances by optimizing a hierarchical conditional random field model. Ninety-two histopathologically confirmed pulmonary nodules were used to evaluate the performance of the proposed APHS algorithm. Further, a comparative study was conducted with two conventional multi-label image segmentation algorithms based on four assessment metrics: the modified Williams index, percentage statistic, overlapping ratio, and difference ratio.
Under the same framework, the proposed APHS algorithm was applied to two clinical applications: multi-label segmentation of nodules with a solid portion and surrounding tissues and pulmonary nodule segmentation. The results obtained indicate that the APHS-generated boundaries are comparable to manual delineations with a modified Williams index of 1.013. Further, the resulting segmentation of the APHS algorithm is also better than that achieved by two conventional multi-label image segmentation algorithms.
The proposed two-level hierarchical segmentation algorithm effectively labelled the pulmonary nodule and its surrounding anatomic structures in lung CT images. This suggests that the generated multi-label structures can potentially serve as the basis for developing related clinical applications.
PMCID: PMC4430912  PMID: 25971587
Lung CT images; Ground-glass nodule segmentation; Statistical region merging; Conditional random field; Hierarchical segmentation tree
8.  Apoptosis pathway signature for prediction of treatment response and clinical outcome in childhood high risk B-Precursor acute lymphoblastic leukemia 
American Journal of Cancer Research  2015;5(5):1844-1853.
The most common cancer in children is acute lymphoblastic leukemia (ALL) and it had high cure rate, especially for B-precursor ALL. However, relapse due to drug resistance and overdose treatment reach the limitations in patient managements. In this study, integration of gene expression microarray data, logistic regression, analysis of microarray (SAM) method, and gene set analysis were performed to discover treatment response associated pathway-based signatures in the original cohort. Results showed that 3772 probes were significantly associated with treatment response. After pathway analysis, only apoptosis pathway had significant association with treatment response. Apoptosis pathway signature (APS) derived from 15 significantly expressed genes had 88% accuracy for treatment response prediction. The APS was further validated in two independent cohorts. Results also showed that APS was significantly associated with induction failure time (adjusted hazard ratio [HR] = 1.60, 95% confidence interval [CI] = [1.13, 2.27]) in the first cohort and significantly associated with event-free survival (adjusted HR = 1.56, 95% CI = [1.13, 2.16]) or overall survival in the second cohort (adjusted HR = 1.74, 95% CI = [1.24, 2.45]). APS not only can predict clinical outcome, but also provide molecular guidance of patient management.
PMCID: PMC4497450  PMID: 26175952
Acute lymphoblastic leukemia; apoptosis; gene signature; prediction and clinical outcome
9.  Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry Can Accurately Differentiate between Mycobacterium masilliense (M. abscessus subspecies bolletti) and M. abscessus (Sensu Stricto) 
Journal of Clinical Microbiology  2013;51(9):3113-3116.
Among 36 Mycobacterium masilliense and 22 M. abscessus isolates identified by erm(41) PCR and sequencing analysis of rpoB and 23S rRNA genes, the rate of accurate differentiation between these two subspecies was 100% by cluster analysis of spectra generated by Bruker Biotyper matrix-assisted laser desorption ionization–time of flight mass spectrometry.
PMCID: PMC3754645  PMID: 23824775
10.  Comparison of the Accuracy of Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry with That of Other Commercial Identification Systems for Identifying Staphylococcus saprophyticus in Urine 
Journal of Clinical Microbiology  2013;51(5):1563-1566.
Among 30 urinary isolates of Staphylococcus saprophyticus identified by sequencing methods, the rate of accurate identification was 100% for Bruker Biotyper matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS), 86.7% for the Phoenix PID and Vitek 2 GP systems, 93.3% for the MicroScan GP33 system, and 46.7% for the BBL CHROMagar Orientation system.
PMCID: PMC3647924  PMID: 23390286
11.  Rho-kinase inhibitor Y-27632 attenuates pulmonary hypertension in hyperoxia-exposed newborn rats 
Acta Pharmacologica Sinica  2013;34(10):1310-1316.
To test the hypothesis that neonatal hyperoxia induced pulmonary hypertension accompanied by increased Rho-kinase expression in rat lungs and that Rho-kinase inhibitor could attenuate right ventricular hypertrophy and pulmonary arterial remodeling.
Newborn rats were exposed to >95% O2 in the first week after birth, then to 60% O2 in the following 2 weeks. Control pups were exposed to room air over the same periods. The pups were injected with either Rho-kinase inhibitor Y-27632 (10 mg·kg−1·d−1, ip) or vehicle from postnatal d 14 to 20. Lung and heart tissues were collected on postnatal d 7 and 21. Rho-kinase activity in lungs was measured using Western blotting and immunohistochemistry. The right ventricular hypertrophy and arterial medial wall thickness (MWT) were assessed morphologically.
Rho-kinase activity in lungs was comparable between the hyperoxic and control pups on postnatal d 7, but it had a more than 2-fold increase in the hyperoxic pups on postnatal d 21. Moreover, the hyperoxic exposure induced structural features of pulmonary hypertension, as shown by the right ventricular hypertrophy and significantly increased arterial MWT. Administration with Y-27632 effectively blocked the hyperoxia-induced increase of Rho-kinase activity in lungs, and attenuated the right ventricular hypertrophy.
Rho-kinase inhibitor may be a novel therapy for attenuating the hyperoxia-induced structural changes in pulmonary hypertension.
PMCID: PMC4002165  PMID: 23974518
neonate; hyperoxia; lung injury; pulmonary hypertension; Rho-kinase; Y-27632
12.  Tissue plasminogen activator attenuates ventilator-induced lung injury in rats 
Acta Pharmacologica Sinica  2012;33(8):991-997.
To test the hypothesis that the tissue plasminogen activator (tPA) may counteract the inhibitory effect of plasminogen activator inhibitors (PAI) and attenuate lung injury in a rat model of ventilator-induced lung injury (VILI).
Adult male Sprague-Dawley rats were ventilated with a HVZP (high-volume zero PEEP) protocol for 2 h at a tidal volume of 30 mL/kg, a respiratory rate of 25 breaths/min, and an inspired oxygen fraction of 21%. The rats were divided into 3 groups (n=7 for each): HVZP+tPA group receiving tPA (1.25 mg/kg, iv) 15 min before ventilation, HVZP group receiving HVZP+vehicle injection, and a control group receiving no ventilation. After 2 h of ventilation, the rats were killed; blood and lungs were collected for biochemical and histological analyses.
HVZP ventilation significantly increased total protein content and the concentration of macrophage inflammatory protein-2 (MIP-2) in the bronchoalveolar lavage fluid (BALF) as well as the lung injury score. Rats that received HVZP ventilation had significantly higher lung PAI-1 mRNA expression, plasma PAI-1 and plasma D-dimer levels than the control animals. tPA treatment significantly reduced the BALF total protein and the lung injury score as compared to the HVZP group. tPA treatment also significantly decreased the plasma D-dimer levels and the HVZP ventilation-induced lung vascular fibrin thrombi. tPA treatment showed no effect on MIP-2 level in BALF.
These results demonstrate that VILI increases lung PAI-1 mRNA expression, plasma levels of PAI-1 and D-dimers, lung injury score and vascular fibrin deposition. tPA can attenuate VILI by decreasing capillary-alveolar protein leakage as well as local and systemic coagulation as shown by decreased lung vascular fibrin deposition and plasma D-dimers.
PMCID: PMC4011324  PMID: 22796762
tissue plasminogen activator (tPA); ventilator-induced lung injury (VILI); plasminogen activator inhibitor-1 (PAI-1); macrophage inflammatory protein-2 (MIP-2); D-dimer; fibrin; bronchoalveolar lavage fluid (BALF)
13.  idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach 
Nucleic Acids Research  2012;40(Web Server issue):W393-W399.
Identification of possible protein targets of small chemical molecules is an important step for unravelling their underlying causes of actions at the molecular level. To this end, we construct a web server, idTarget, which can predict possible binding targets of a small chemical molecule via a divide-and-conquer docking approach, in combination with our recently developed scoring functions based on robust regression analysis and quantum chemical charge models. Affinity profiles of the protein targets are used to provide the confidence levels of prediction. The divide-and-conquer docking approach uses adaptively constructed small overlapping grids to constrain the searching space, thereby achieving better docking efficiency. Unlike previous approaches that screen against a specific class of targets or a limited number of targets, idTarget screen against nearly all protein structures deposited in the Protein Data Bank (PDB). We show that idTarget is able to reproduce known off-targets of drugs or drug-like compounds, and the suggested new targets could be prioritized for further investigation. idTarget is freely available as a web-based server at
PMCID: PMC3394295  PMID: 22649057
14.  Inferring Genetic Interactions via a Data-Driven Second Order Model 
Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R3) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm.
PMCID: PMC3342528  PMID: 22563331
gene expression; genetic interaction; microarray data; pathway; regression; transcriptional regulatory interaction
15.  Patient-oriented simulation based on Monte Carlo algorithm by using MRI data 
Although Monte Carlo simulations of light propagation in full segmented three-dimensional MRI based anatomical models of the human head have been reported in many articles. To our knowledge, there is no patient-oriented simulation for individualized calibration with NIRS measurement. Thus, we offer an approach for brain modeling based on image segmentation process with in vivo MRI T1 three-dimensional image to investigate the individualized calibration for NIRS measurement with Monte Carlo simulation.
In this study, an individualized brain is modeled based on in vivo MRI 3D image as five layers structure. The behavior of photon migration was studied for this individualized brain detections based on three-dimensional time-resolved Monte Carlo algorithm. During the Monte Carlo iteration, all photon paths were traced with various source-detector separations for characterization of brain structure to provide helpful information for individualized design of NIRS system.
Our results indicate that the patient-oriented simulation can provide significant characteristics on the optimal choice of source-detector separation within 3.3 cm of individualized design in this case. Significant distortions were observed around the cerebral cortex folding. The spatial sensitivity profile penetrated deeper to the brain in the case of expanded CSF. This finding suggests that the optical method may provide not only functional signal from brain activation but also structural information of brain atrophy with the expanded CSF layer. The proposed modeling method also provides multi-wavelength for NIRS simulation to approach the practical NIRS measurement.
In this study, the three-dimensional time-resolved brain modeling method approaches the realistic human brain that provides useful information for NIRS systematic design and calibration for individualized case with prior MRI data.
PMCID: PMC3355000  PMID: 22510474
Patient-oriented simulation; Time-resolved Monte Carlo; Brain modeling; Spatial sensitivity profile
16.  Postnatal Corticosteroids for Prevention and Treatment of Chronic Lung Disease in the Preterm Newborn 
Despite significant progress in the treatment of preterm neonates, bronchopulmonary dysplasia (BPD) continues to be a major cause of neonatal morbidity. Affected infants suffered from long-term pulmonary and nonpulmonary sequel. The pulmonary sequels include reactive airway disease and asthma during childhood and adolescence. Nonpulmonary sequels include poor coordination and muscle tone, difficulty in walking, vision and hearing problems, delayed cognitive development, and poor academic achievement. As inflammation seems to be a primary mediator of injury in pathogenesis of BPD, role of steroids as antiinflammatory agent has been extensively studied and proven to be efficacious in management. However, evidence is insufficient to make a recommendation regarding other glucocorticoid doses and preparations. Numerous studies have been performed to investigate the effects of steroid. The purpose of this paper is to evaluate these studies in order to elucidate the beneficial and harmful effects of steroid on the prevention and treatment of BPD.
PMCID: PMC3189570  PMID: 22007245
17.  Inferring genetic interactions via a nonlinear model and an optimization algorithm 
BMC Systems Biology  2010;4:16.
Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.
An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.
GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.
PMCID: PMC2848194  PMID: 20184777
18.  Uncovering transcriptional interactions via an adaptive fuzzy logic approach 
BMC Bioinformatics  2009;10:400.
To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy.
AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF), and using 19 transcription factors (TFs), we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms.
AdaFuzzy successfully integrates multiple types of data (sequence, ChIP, and microarray) to predict transcriptional regulatory networks. The validated success in the prediction results implies that AdaFuzzy can be applied to uncover TIs in yeast.
PMCID: PMC2797023  PMID: 19961622
19.  Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling 
BMC Bioinformatics  2008;9:134.
With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.
Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.
SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.
PMCID: PMC2323972  PMID: 18312694

Results 1-19 (19)