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Artificial intelligence in cancer research: learning at different levels of data granularity
dc.contributor.author | Cirillo, Davide |
dc.contributor.author | Nuñez Carpintero, Iker |
dc.contributor.author | Valencia, Alfonso |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2021-03-22T16:29:52Z |
dc.date.available | 2021-03-22T16:29:52Z |
dc.date.issued | 2021 |
dc.identifier.citation | Cirillo, D.; Nuñez Carpintero, I.; Valencia, A. Artificial intelligence in cancer research: learning at different levels of data granularity. "Molecular Oncology", 2021, |
dc.identifier.issn | 1574-7891 |
dc.identifier.uri | http://hdl.handle.net/2117/342178 |
dc.description.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. |
dc.description.sponsorship | This work was supported by the European Union's Horizon 2020 research and innovation program under grant agreements 826121 (‘iPC—individualizedPaediatricCure: cloud‐based virtual‐patient models for precision pediatric oncology’). |
dc.format.extent | 13 p. |
dc.language.iso | eng |
dc.publisher | Wiley |
dc.rights | Attribution 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica |
dc.subject.lcsh | Cancer research |
dc.subject.lcsh | Machine learning |
dc.subject.other | Artificial intelligence |
dc.subject.other | Cancer research |
dc.subject.other | Data granularity |
dc.subject.other | Machine learning |
dc.title | Artificial intelligence in cancer research: learning at different levels of data granularity |
dc.type | Article |
dc.subject.lemac | Intel.ligència artificial |
dc.identifier.doi | 10.1002/1878-0261.12920 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://febs.onlinelibrary.wiley.com/doi/10.1002/1878-0261.12920 |
dc.rights.access | Open Access |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/826121/EU/individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology/iPC |
local.citation.publicationName | Molecular Oncology |
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