Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes

Combining path consistency (PC) algorithms with conditional mutual information (CMI) are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discrimin...

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Veröffentlicht in:PloS one Jg. 11; H. 5; S. e0154953
Hauptverfasser: Xiao, Fei, Gao, Lin, Ye, Yusen, Hu, Yuxuan, He, Ruijie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 12.05.2016
Public Library of Science (PLoS)
Schlagworte:
Algorithms
Analysis
Artificial intelligence
Binding sites
Bioinformatics
Biology
Biology and Life Sciences
Complexity
Computer and Information Sciences
Computer science
Computer Simulation
Correlation analysis
Correlation coefficient
Correlation coefficients
Databases, Genetic
Gene expression
Gene Expression Regulation
Gene Regulatory Networks
Genes
Genetic Association Studies
Genetic regulation
Genomes
Humans
Hypotheses
Information theory
Leukemia
Leukemia, Myeloid, Acute - genetics
Linear programming
Medicine and Health Sciences
Networks
Normal distribution
Personal computers
Physical Sciences
Principal Component Analysis
Regulations
Research and Analysis Methods
ROC Curve
Saccharomyces cerevisiae - genetics
Taiwan
China
ISSN:1932-6203, 1932-6203
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Zusammenfassung:Combining path consistency (PC) algorithms with conditional mutual information (CMI) are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discriminate the direct regulations from indirect ones. However, it is still a challenge to select the conditional genes in an optimal way, which affects the performance and computation complexity of the PC algorithm. In this study, we develop a novel conditional mutual information-based algorithm, namely RPNI (Regulation Pattern based Network Inference), to infer gene regulatory networks. For conditional gene selection, we define the co-regulation pattern, indirect-regulation pattern and mixture-regulation pattern as three candidate patterns to guide the selection of candidate genes. To demonstrate the potential of our algorithm, we apply it to gene expression data from DREAM challenge. Experimental results show that RPNI outperforms existing conditional mutual information-based methods in both accuracy and time complexity for different sizes of gene samples. Furthermore, the robustness of our algorithm is demonstrated by noisy interference analysis using different types of noise.
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Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: FX YSY. Performed the experiments: FX YSY. Analyzed the data: FX YSY. Contributed reagents/materials/analysis tools: FX YSY. Wrote the paper: FX YSY YXH LG. Paper polishing: LG YXH. Equation editing: RJH FX YSY.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0154953