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Inference of Gene Regulatory Network Based on Local Bayesian Networks
- Source :
- PLoS Computational Biology, Vol 12, Iss 8, p e1005024 (2016), PLoS Computational Biology
- Publication Year :
- 2016
- Publisher :
- Public Library of Science (PLoS), 2016.
-
Abstract
- The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce the computational cost of BN due to much smaller sizes of local GRNs, but also identify the directions of the regulations.<br />Author Summary Gene regulatory network (GRN) represents how some genes encode regulatory molecules such as transcription factors or microRNAs for regulating the expression of other genes. Accurate inference of GRN is an important task to understand the biological activity from signal emulsion to metabolic dynamics, prioritize potential drug targets of various diseases, devise effective therapeutics, and discover the novel pathways. In this paper, we propose a novel local Bayesian network (LBN) algorithm to improve the accuracy of GRN inference from gene expression data by exploring advantages of Bayesian network (BN) and conditional mutual information (CMI) methods. BNs with kNN network decomposition and CMI are respectively introduced to reduce the high computational complexity of BN and remove the false or redundant regulation interactions. The superior performance of the proposed LBN approach is demonstrated on GRN datasets from DREAM challenge as well as the SOS DNA repair network in E. coli.
- Subjects :
- 0301 basic medicine
Computational complexity theory
Computer science
Gene regulatory network
Inference
Gene Expression
02 engineering and technology
Genetic Networks
computer.software_genre
Information theory
Infographics
Bayes' theorem
Gene Regulatory Networks
Biology (General)
Ecology
Conditional mutual information
Applied Mathematics
Simulation and Modeling
Genomics
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Data mining
Graphs
Network Analysis
Algorithms
Research Article
Computer and Information Sciences
QH301-705.5
0206 medical engineering
Machine learning
Research and Analysis Methods
03 medical and health sciences
Cellular and Molecular Neuroscience
Gene Types
Genetics
Escherichia coli
Gene Regulation
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Models, Genetic
business.industry
Data Visualization
Gene Expression Profiling
Bayesian network
Biology and Life Sciences
Computational Biology
Bayes Theorem
Genome Analysis
Probability Theory
Probability Distribution
030104 developmental biology
Exponential search
Regulator Genes
Artificial intelligence
business
computer
020602 bioinformatics
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 12
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....25c10760cafc07951413e0bba99a42f5