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Open AccessArticle

System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork

1
Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8507, Japan
2
RIKEN Cluster for Science, Technology and Innovation Hub, Medical Sciences Innovation Hub Program, Kanagawa 230-0045, Japan
3
Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
*
Author to whom correspondence should be addressed.
Biomolecules 2020, 10(2), 306; https://doi.org/10.3390/biom10020306 (registering DOI)
Received: 17 January 2020 / Revised: 3 February 2020 / Accepted: 8 February 2020 / Published: 14 February 2020
(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)
Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed Edge Contribution value (ECv) based on the estimated system, which realizes condition-specific subnetwork extraction using a limited number of samples. We applied this method to the Epithelial-Mesenchymal Transition (EMT) data set that is related to the process of metastasis and thus prognosis in cancer biology. We established our method-driven EMT network representing putative gene interactions. Furthermore, we found that the sample-specific ECv patterns of this EMT network can characterize the survival of lung cancer patients. These results show that our method unveils the explainable network differences in biological and clinical features through artificial intelligence technology.
Keywords: gene network; differential network analysis; lung cancer survival analysis; EMT gene network; differential network analysis; lung cancer survival analysis; EMT
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MDPI and ACS Style

Tanaka, Y.; Tamada, Y.; Ikeguchi, M.; Yamashita, F.; Okuno, Y. System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork. Biomolecules 2020, 10, 306.

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