Abstract
Modeling-based anti-cancer drug sensitivity prediction has been extensively studied in recent years. While most drug sensitivity prediction models only use gene expression data, the remarkable impacts of gene mutation, methylation, and copy number variation on drug sensitivity are neglected. Drug sensitivity prediction can both help protect patients from some adverse drug reactions and improve the efficacy of treatment. Genomics data are extremely useful for drug sensitivity prediction task. This article reviews the role of drug sensitivity prediction, describes a variety of methods for predicting drug sensitivity. Moreover, the research significance of drug sensitivity prediction, as well as existing problems are well discussed.
Highlights
With the significant technological advancements, a variety of modalities have been developed to predict the sensitivity of tumors to anti-cancer drugs, which can improve drug efficacy and reduce adverse effects and the financial burden of treatment on patient
We describe the process of models, including the construction of a time-series biological network and the evolution prediction of the tumor drug resistance state via k-means++ clustering, random walk, and other machine learning methods (Oxnard and Geoffrey, 2016; Hangauer et al, 2017; Recasens and Munoz, 2019; Yu et al, 2020a; Cheng et al, 2021)
1) The first aspect is mainly concerned with the analysis of different drug resistance mechanisms arising from pan-cancer and tumor drug resistance based on the different tumor cell lines after treatment
Summary
With the significant technological advancements, a variety of modalities have been developed to predict the sensitivity of tumors to anti-cancer drugs, which can improve drug efficacy and reduce adverse effects and the financial burden of treatment on patient. The sensitivity of tumors to anti-cancer drugs can be assessed by using patient cell lines, which can facilitate the use of synergistic regimens (Liu et al, 2016). We describe the strategy of sensitivity prediction of tumors to anti-cancer drugs using graph representation learning This strategy can explain the mechanism by which cancer develops, and most importantly provide reliable evidence for cancer treatment to promote the development of bioinformatics. We discuss the strategy of developing a collaborative drug sensitivity analysis platform that can provide specific cancer cell lines with optimal stimulatory or inhibitory candidate drug molecules.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
