Artificial intelligence in cancer research: learning at different levels of data granularity
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Cita com:
hdl:2117/342178
Tipus de documentArticle
Data publicació2021
EditorWiley
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement 3.0 Espanya
Abstract
From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.
CitacióCirillo, D.; Nuñez Carpintero, I.; Valencia, A. Artificial intelligence in cancer research: learning at different levels of data granularity. "Molecular Oncology", 2021,
ISSN1574-7891
Versió de l'editorhttps://febs.onlinelibrary.wiley.com/doi/10.1002/1878-0261.12920
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