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

Authors
  • Liu, Wenting1, 2
  • Rajapakse, Jagath C.3
  • 1 School of Public Health and Management, Hubei University of Medicine, Shiyan, Hubei, China , Shiyan (China)
  • 2 Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA , Los Angeles (United States)
  • 3 School of Computer Engineering, Nanyang Technological University, Singapore, Singapore , Singapore (Singapore)
Type
Published Article
Journal
BMC Systems Biology
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Apr 05, 2019
Volume
13
Issue
Suppl 2
Identifiers
DOI: 10.1186/s12918-019-0695-x
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundSystematic 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.ResultsWe 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.ConclusionThe 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.

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