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