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タイトル: | DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction |
著者: | Guvencpaltun, Betul Kaski, Samuel Mamitsuka, Hiroshi https://orcid.org/0000-0002-6607-5617 (unconfirmed) |
著者名の別形: | 馬見塚, 拓 |
キーワード: | Drugs Cancer Bioinformatics Data models Bayes methods Chemicals Proteins Personalized medicine drug response prediction machine learning data integration |
発行日: | Jul-2022 |
出版者: | Institute of Electrical and Electronics Engineers (IEEE) |
誌名: | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
巻: | 19 |
号: | 4 |
開始ページ: | 2197 |
終了ページ: | 2207 |
抄録: | Detecting predictive biomarkers from multi-omics data is important for precision medicine, to improve diagnostics of complex diseases and for better treatments. This needs substantial experimental efforts that are made difficult by the heterogeneity of cell lines and huge cost. An effective solution is to build a computational model over the diverse omics data, including genomic, molecular, and environmental information. However, choosing informative and reliable data sources from among the different types of data is a challenging problem. We propose DIVERSE, a framework of Bayesian importance-weighted tri- and bi-matrix factorization(DIVERSE3 or DIVERSE2) to predict drug responses from data of cell lines, drugs, and gene interactions. DIVERSE integrates the data sources systematically, in a step-wise manner, examining the importance of each added data set in turn. More specifically, we sequentially integrate five different data sets, which have not all been combined in earlier bioinformatic methods for predicting drug responses. Empirical experiments show that DIVERSE clearly outperformed five other methods including three state-of-the-art approaches, under cross-validation, particularly in out-of-matrix prediction, which is closer to the setting of real use cases and more challenging than simpler in-matrix prediction. Additionally, case studies for discovering new drugs further confirmed the performance advantage of DIVERSE. |
著作権等: | This work is licensed under a Creative Commons Attribution 4.0 License. |
URI: | http://hdl.handle.net/2433/275805 |
DOI(出版社版): | 10.1109/tcbb.2021.3065535 |
PubMed ID: | 33705322 |
出現コレクション: | 学術雑誌掲載論文等 |
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