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タイトル: | System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork |
著者: | Tanaka, Yoshihisa Tamada, Yoshinori Ikeguchi, Marie Yamashita, Fumiyoshi https://orcid.org/0000-0002-3503-8696 (unconfirmed) Okuno, Yasushi |
著者名の別形: | 田中, 良尚 玉田, 嘉紀 池口, 茉莉恵 山下, 富義 奥野, 恭史 |
キーワード: | gene network differential network analysis lung cancer survival analysis EMT |
発行日: | Feb-2020 |
出版者: | MDPI AG |
誌名: | Biomolecules |
巻: | 10 |
号: | 2 |
論文番号: | 306 |
抄録: | 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. |
著作権等: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
URI: | http://hdl.handle.net/2433/250777 |
DOI(出版社版): | 10.3390/biom10020306 |
PubMed ID: | 32075209 |
出現コレクション: | 学術雑誌掲載論文等 |
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