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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.

Supplementary Material

Supplementary figures provide the time series gene expression data to which MANI and other gene network inference techniques were applied with adipogenesis gene expression data shown in Supplemental Figure S1, and DREAM3 and DREAM4 gene expression data in Supplemental Figures S2 and S3, respectively. The supplementary tables provide the correlation matrices between genes in the adipogenesis network (Supplementary Table S1) and DREAM3 network (Supplementary Table S2). These matrices helped create the modules (metaphorical gene windows) based on which the networks were constructed. The gene windows and the kinetic parameters estimated by MANI for the DREAM3 network are shown in Supplementary Tables S3 and S4, respectively. The true regulatory relationships between genes in the DREAM4 network are listed in Supplementary Table S5. Furthermore, the derivation of the formula to estimate the likelihood that a certain mathematical model captures the gene regulatory relationship between three genes in a given window is described in the supplementary material.

  1. Supplementary Material