Wei Zhang, Wenchao Li, Jianming Zhang* and Ning Wang Pages 255 - 268 ( 14 )
Background: Gene Regulatory Network (GRN) inference algorithms aim to explore casual interactions between genes and transcriptional factors. High-throughput transcriptomics data including DNA microarray and single cell expression data contain complementary information in network inference.
Objective: To enhance GRN inference, data integration across various types of expression data becomes an economic and efficient solution.
Method: In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is proposed to merge complementary information from microarray and single cell expression data. This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively evaluates the credibility levels of each information source and determines the final ranked list.
Results: Two groups of in silico gene networks are applied to illustrate the effectiveness of the proposed E-alpha integration. Experimental outcomes with size50 and size100 in silico gene networks suggest that the proposed E-alpha rule significantly improves performance metrics compared with single information source.
Conclusion: In GRN inference, the integration of hybrid expression data using E-alpha rule provides a feasible and efficient way to enhance performance metrics than solely increasing sample sizes.
Gene regulatory network, ensemble inference, gradient boosting tree, data integration.
Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310013, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310013, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310013, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310013