Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular...
Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles
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Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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Contents
(1) Background: Inter-tumour heterogeneity is one of cancer’s most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a p...
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Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles
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TN_cdi_doaj_primary_oai_doaj_org_article_865f23335498479d9afd376cc1ca8346
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_865f23335498479d9afd376cc1ca8346
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ISSN
2227-9059
E-ISSN
2227-9059
DOI
10.3390/biomedicines9101319
