TY - JOUR
T1 - Inferring Genome-Wide Interaction Networks Using the Phi-Mixing Coefficient, and Applications to Lung and Breast Cancer
AU - Singh, Nitin
AU - Ahsen, Mehmet Eren
AU - Challapalli, Niharika
AU - Kim, Hyun Seok
AU - White, Michael A.
AU - Vidyasagar, Mathukumalli
N1 - Funding Information:
The work of H.-S. Kim was supported in part by the National Research and Development Program for Cancer Control, Ministry of Health and Welfare, South Korea, under Grant 1420100, and in part by CPRIT Training under Grant RP101496. The work of M. A. White was supported in part by the Welch Foundation under Award I-1414, and in part by NIH under Grant CA197717 and Grant CA176284. The associate editor coordinating the review of this article and approving it for publication was C.-B. Chae.
Publisher Copyright:
© 2015 IEEE.
PY - 2018/9
Y1 - 2018/9
N2 - Constructing gene interaction networks (GINs) from high-throughput gene expression data is an important and challenging problem in systems biology. Existing algorithms produce networks that either have undirected and unweighted edges, or else are constrained to contain no cycles, both of which are biologically unrealistic. In the present paper, we propose a new algorithm, based on a concept from probability theory known as the $\phi $-mixing coefficient, that produces networks whose edges are weighted and directed, and are permitted to contain cycles. Specifically, we inferred networks for two subtypes of lung cancer small cell (SCLC) and non-small cell (NSCLC) as well as normal lung tissue. Then, we compared the outcomes of siRNA screening of 19,000+ genes on 11 NSCLC cell lines, and found that the higher the degree of a gene in the inferred network, the more essential it is to the survival of a cell. We also analyzed data from a ChIP-Seq experiment to determine putative downstream targets of ASCL1. The SCLC network was enriched for ChIP-seq neighbors of this oncogenic transcription factor, but not in the NSCLC network. We also reverse-engineered whole-genome interaction networks for two distinct subtypes of breast cancer, namely Luminal-A and Basal (also known as triple negative).
AB - Constructing gene interaction networks (GINs) from high-throughput gene expression data is an important and challenging problem in systems biology. Existing algorithms produce networks that either have undirected and unweighted edges, or else are constrained to contain no cycles, both of which are biologically unrealistic. In the present paper, we propose a new algorithm, based on a concept from probability theory known as the $\phi $-mixing coefficient, that produces networks whose edges are weighted and directed, and are permitted to contain cycles. Specifically, we inferred networks for two subtypes of lung cancer small cell (SCLC) and non-small cell (NSCLC) as well as normal lung tissue. Then, we compared the outcomes of siRNA screening of 19,000+ genes on 11 NSCLC cell lines, and found that the higher the degree of a gene in the inferred network, the more essential it is to the survival of a cell. We also analyzed data from a ChIP-Seq experiment to determine putative downstream targets of ASCL1. The SCLC network was enriched for ChIP-seq neighbors of this oncogenic transcription factor, but not in the NSCLC network. We also reverse-engineered whole-genome interaction networks for two distinct subtypes of breast cancer, namely Luminal-A and Basal (also known as triple negative).
KW - Computational biology
KW - genetic communication
KW - random variables
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U2 - 10.1109/TMBMC.2019.2933391
DO - 10.1109/TMBMC.2019.2933391
M3 - Article
AN - SCOPUS:85077500138
SN - 2332-7804
VL - 4
SP - 123
EP - 139
JO - IEEE Transactions on Molecular, Biological, and Multi-Scale Communications
JF - IEEE Transactions on Molecular, Biological, and Multi-Scale Communications
IS - 3
M1 - 8789692
ER -