健康・医療情報学ジャーナル

Decomposition Approach for Learning Large Gene Regulatory Network

Abstract

Leung-Yau Lo, Man-Leung Wong, Kwong-Sak Leung, Wing-Lun Lam and Chi-Wai Chung

Gene Regulatory Network (GRN) represents the complex interaction between Transcription Factors (TFs) and other genes with time delays. They are important in the working of the cell. Learning GRN is an important first step towards understanding the working of the cell and consequently curing diseases related to malfunctioning of the cell. One significant problem in learning GRN is that the available time series expression data is still limited compared to the network size. To alleviate this problem, besides using multiple expression replicates, we propose to decompose large network into small subnetwork without prior knowledge. Our algorithm first infers an initial GRN using CLINDE, then decomposes it into possibly overlapping subnetworks, then infers each subnetwork by either CLINDE or DD-lasso and finally merges the subnetworks. We have tested this algorithm on synthetic data of many networks with 500 and 1000 genes. We have also tested on real data on 41 human TF regulatory networks. Results show that our proposed algorithm does improve the GRN learning performance of using either CLINDE or DD-lasso alone on the large network.