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International Journal of Genomics
Volume 2017, Article ID 8514071, 9 pages
https://doi.org/10.1155/2017/8514071
Research Article

Module Anchored Network Inference: A Sequential Module-Based Approach to Novel Gene Network Construction from Genomic Expression Data on Human Disease Mechanism

1Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
2Department of Medicine, Division of Endocrinology and Metabolism, University of Virginia, Charlottesville, VA 22908, USA
3Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, USA
4Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA 22908, USA

Correspondence should be addressed to Annamalai Muthiah; ude.ainigriv@at2ma

Received 1 October 2016; Accepted 15 December 2016; Published 18 January 2017

Academic Editor: Lam C. Tsoi

Copyright © 2017 Annamalai Muthiah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Different computational approaches have been examined and compared for inferring network relationships from time-series genomic data on human disease mechanisms under the recent Dialogue on Reverse Engineering Assessment and Methods (DREAM) challenge. Many of these approaches infer all possible relationships among all candidate genes, often resulting in extremely crowded candidate network relationships with many more False Positives than True Positives. To overcome this limitation, we introduce a novel approach, Module Anchored Network Inference (MANI), that constructs networks by analyzing sequentially small adjacent building blocks (modules). Using MANI, we inferred a 7-gene adipogenesis network based on time-series gene expression data during adipocyte differentiation. MANI was also applied to infer two 10-gene networks based on time-course perturbation datasets from DREAM3 and DREAM4 challenges. MANI well inferred and distinguished serial, parallel, and time-dependent gene interactions and network cascades in these applications showing a superior performance to other in silico network inference techniques for discovering and reconstructing gene network relationships.