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Title: SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
Authors: Preto, Antonio J. 
Matos-Filipe, Pedro 
Mourão, Joana 
Moreira, Irina S. 
Keywords: biophysics; cancer; drug synergy; ensemble learning; interpretability; omics
Issue Date: 2022
Publisher: Oxford University Press
Project: LA/P/0058/2020 (CIBB) 
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP/04539/2020/PT 
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB/04539/2020/PT 
POCI-01-0145-FEDER-031356- Deep learning in the discovery of cancer medicines: a pipeline for the generation of new therapies 
DSAIPA/DS/0118/2020 - Cutting-Edge Virus-Host Interactome Discovery: A Multi- Omics AI Driven Approach 
info:eu-repo/grantAgreement/FCT/POR_CENTRO/SFRH/BD/144966/2019/PT/Deep-Learning application to in silico Drug Design 
Serial title, monograph or event: GigaScience
Volume: 11
Abstract: In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)-powered informational view that integrates the relevant scientific fields and explores new territories.
ISSN: 2047-217X
DOI: 10.1093/gigascience/giac087
Rights: openAccess
Appears in Collections:I&D CNC - Artigos em Revistas Internacionais
FCTUC Ciências da Vida - Artigos em Revistas Internacionais
I&D CIBB - Artigos em Revistas Internacionais

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