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• W3204129839 abstract "Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various forms. A notable use of omics data beyond routine data mining is to use them for computational modeling that allows generating useful predictions, such as drug responses and prognostic biomarkers. In particular, an increasing volume of omics data has facilitated the development of machine learning models. In this mini review, we highlight recent studies on the use of multi-omics data for studying drug-resistant cancer cells. We put a particular focus on studies that use computational models to characterize drug-resistant cancer cells, and to predict biomarkers and/or drug responses. Computational models covered in this mini review include network-based models, machine learning models and genome-scale metabolic models. We also provide perspectives on future research opportunities for combating drug-resistant cancer cells." @default.
• W3204129839 created "2021-10-11" @default.
• W3204129839 creator A5026878735 @default.
• W3204129839 creator A5048072583 @default.
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• W3204129839 date "2021-10-06" @default.
• W3204129839 modified "2023-10-17" @default.
• W3204129839 title "Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells" @default.
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• W3204129839 doi "https://doi.org/10.3389/fgene.2021.742902" @default.
• W3204129839 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8527086" @default.
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• W3204129839 hasPublicationYear "2021" @default.
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