Decomposition Approach for Learning Large Gene Regulatory Network
Received Date: Jun 08, 2018 / Accepted Date: Jun 18, 2018 / Published Date: Jun 20, 2018
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.
Keywords: Gene regulatory network; Causal learning; Time series expression data
Citation: Lo LY, Wongy ML, Leungz KS, Lamx WL, Chung CW (2018) Decomposition Approach for Learning Large Gene Regulatory Network. J Health Med Informat 9: 315. Doi: 10.4172/2157-7420.1000315
Copyright: © 2018 Lo LY, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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