Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells

Frontiers in Genetics

Hyun Uk Kim¹  Hae Deok Jung²  Yoo Jin Sung² 

[1] BioProcess Engineering Research Center and BioInformatics Research Center KAIST, Daejeon, South Korea;Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea;KAIST Institute for Artificial Intelligence, KAIST, Daejeon, South Korea;

关键词: cancer;    drug resistance;    omics;    computational modeling;    network-based model;    machine learning;   

DOI  :  10.3389/fgene.2021.742902

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown