Machine learning approaches to predict drug efficacy and toxicity in oncology
Name:
36936080.pdf
Size:
1.808Mb
Format:
PDF
Description:
Identified with Open Access button
Affiliation
Intelligencia Inc, New York, NY 10014, USAIssue Date
2023
Metadata
Show full item recordAbstract
In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug repurposing, diagnostics, clinical trial design, and pharmaceutical production. MLAs have the potential to provide valuable insights and predictions in these areas by representing both the disease state and the therapeutic agents used to treat it. To fully utilize the capabilities of MLAs in oncology, it is important to understand the fundamental concepts underlying these algorithms and how they can be applied to assess the efficacy and toxicity of therapeutics. In this perspective, we lay out approaches to represent both the disease state and the therapeutic agents used by MLAs to derive novel insights and make relevant predictions.Citation
Badwan BA, Liaropoulos G, Kyrodimos E, Skaltsas D, Tsirigos A, Gorgoulis VG. Machine learning approaches to predict drug efficacy and toxicity in oncology. Cell reports methods. 2023 Feb 27;3(2):100413. PubMed PMID: 36936080. Pubmed Central PMCID: PMC10014302. Epub 2023/03/21. eng.Journal
Cell Reports MethodsDOI
10.1016/j.crmeth.2023.100413PubMed ID
36936080Additional Links
https://dx.doi.org/10.1016/j.crmeth.2023.100413Type
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1016/j.crmeth.2023.100413
Scopus Count
Collections
Related articles
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.
- Authors: Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P
- Issue date: 2021 Aug
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.
- Authors: Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou MM, Zhang B
- Issue date: 2021 May
- Prediction of chronic kidney disease and its progression by artificial intelligence algorithms.
- Authors: Schena FP, Anelli VW, Abbrescia DI, Di Noia T
- Issue date: 2022 Nov
- Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery.
- Authors: Yang X, Wang Y, Byrne R, Schneider G, Yang S
- Issue date: 2019 Sep 25
- Machine learning approaches and their applications in drug discovery and design.
- Authors: Priya S, Tripathi G, Singh DB, Jain P, Kumar A
- Issue date: 2022 Jul