Show simple item record

dc.contributor.authorVergetis, V.
dc.contributor.authorSkaltsas, D.
dc.contributor.authorGorgoulis, Vassilis G
dc.contributor.authorTsirigos, A.
dc.date.accessioned2021-04-20T08:08:25Z
dc.date.available2021-04-20T08:08:25Z
dc.date.issued2021en
dc.identifier.citationVergetis V, Skaltsas D, Gorgoulis VG, Tsirigos A. Assessing Drug Development Risk Using Big Data and Machine Learning. Cancer Res. 2021;81(4):816-9.en
dc.identifier.pmid33355183en
dc.identifier.doi10.1158/0008-5472.Can-20-0866en
dc.identifier.urihttp://hdl.handle.net/10541/623963
dc.description.abstractIdentifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard given the complexities of drug biology and clinical trials. This inherent risk is often misunderstood and mischaracterized, leading to inefficient allocation of resources and, as a result, an overall reduction in R&D productivity. Here we argue that the recent resurgence of Machine Learning in combination with the availability of data can provide a more accurate and unbiased estimate of drug development risk.en
dc.language.isoenen
dc.relation.urlhttps://dx.doi.org/10.1158/0008-5472.Can-20-0866en
dc.titleAssessing drug development risk using big data and machine learningen
dc.typeArticleen
dc.contributor.departmentIntelligencia Inc., New York, New York.en
dc.identifier.journalCancer Researchen
dc.description.noteen]


This item appears in the following Collection(s)

Show simple item record