Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches.

ABSTRACT: Predicting the response of cancer cell lines to specific drugs is one of the central problems in personalized medicine, where the cell lines show diverse characteristics. Researchers have developed a variety of computational methods to discover associations between drugs and cell lines, and improved drug sensitivity analyses by integrating heterogeneous biological data. However, choosing informative data sources and methods that can incorporate multiple sources efficiently is the challenging part of successful analysis in personalized medicine. The reason is that finding decisive factors of cancer and developing methods that can overcome the problems of integrating data, such as differences in data structures and data complexities, are difficult. In this review, we summarize recent advance

SUBMITTER: Guvenc Paltun B 

PROVIDER: S-EPMC7820853 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature