Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks

Autor: Wenting Liu, Jagath C. Rajapakse
Přispěvatelé: School of Computer Science and Engineering
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Computer science
Systems biology
0206 medical engineering
Bayesian probability
Normal Distribution
Gene regulatory network
Inference
02 engineering and technology
computer.software_genre
Gene regulatory network (GRN)
Gene Regulatory Network (GRN)
Structural Biology
Prior probability
Gene Regulatory Networks
Protein Interaction Maps
Hidden Markov model
Molecular Biology
lcsh:QH301-705.5
Engineering::Computer science and engineering [DRNTU]
Transitive relation
Research
Gene Expression Profiling
Systems Biology
Applied Mathematics
Bayes Theorem
Transitive protein-protein interactions
Mixture model
Computer Science Applications
Protein-protein interaction networks
Gaussian mixture model (GMM)
lcsh:Biology (General)
Modeling and Simulation
Gene Expressions
Data mining
Gene expressions
computer
020602 bioinformatics
Zdroj: BMC Systems Biology, Vol 13, Iss S2, Pp 1-13 (2019)
BMC Systems Biology
ISSN: 1752-0509
DOI: 10.1186/s12918-019-0695-x
Popis: Background: Systematic fusion of multiple data sources for Gene Regulatory Networks [GRN] inference remains a key challenge in systems biology. We incorporate information from protein-protein interaction networks [PPIN] into the process of GRN inference from gene expression [GE] data. However, existing PPIN remain sparse and transitive protein interactions can help predict missing protein interactions. We therefore propose a systematic probabilistic framework on fusing GE data and transitive protein interaction data to coherently build GRN. Results: We use a Gaussian Mixture Model [GMM] to soft-cluster GE data, allowing overlapping cluster memberships. Next, a heuristic method is proposed to extend sparse PPIN by incorporating transitive linkages. We then propose a novel way to score extended protein interactions by combining topological properties of PPIN and correlations of GE. Following this, GE data and extended PPIN are fused using a Gaussian Hidden Markov Model [GHMM] in order to identify gene regulatory pathways and refine interaction scores that are then used to constrain the GRN structure. We employ a Bayesian Gaussian Mixture [BGM] model to refine the GRN derived from GE data by using the structural priors derived from GHMM. Experiments on real yeast regulatory networks demonstrate both the feasibility of the extended PPIN in predicting transitive protein interactions and its effectiveness on improving the coverage and accuracy the proposed method of fusing PPIN and GE to build GRN. Conclusion: The GE and PPIN fusion model outperforms both the state-of-the-art single data source models [CLR, GENIE3, TIGRESS] as well as existing fusion models under various constraints. MOE (Min. of Education, S’pore) Published version
Databáze: OpenAIRE
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