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Abstract
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.
Original language | English |
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Pages (from-to) | 2197-2207 |
Number of pages | 11 |
Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Volume | 19 |
Issue number | 4 |
DOIs | |
Publication status | Published - 11 Apr 2022 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Bayes methods
- Bayesian methods
- Bioinformatics
- Cancer
- Chemicals
- data integration
- Data models
- drug response prediction
- Drugs
- machine learning
- Personalized medicine
- Proteins
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Dive into the research topics of 'DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction'. Together they form a unique fingerprint.Projects
- 1 Finished
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-: Intelligent Crop Production: Data-integrative, Multi-task Learning Meets Crop Simulator
Mamitsuka, H. (Principal investigator), Nariman Zadeh, H. (Project Member), Strahl, J. (Project Member), Guvenc, B. (Project Member), Ji, S. (Project Member), Rissanen, S. (Project Member), Honkamaa, J. (Project Member), Pöllänen, A. (Project Member), Hiremath, S. (Project Member) & Ojala, F. (Project Member)
01/01/2018 → 31/12/2022
Project: Academy of Finland: Other research funding