publication . Article . 2021

Artificial intelligence in cancer research: learning at different levels of data granularity

Davide Cirillo; Iker Núñez-Carpintero; Alfonso Valencia;
Open Access English
  • Published: 01 Feb 2021 Journal: Molecular Oncology, volume 15, issue 4, pages 817-829 (issn: 1574-7891, eissn: 1878-0261, Copyright policy)
  • Publisher: John Wiley and Sons Inc.
  • Country: Spain
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 hete...
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ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: Review, artificial intelligence, cancer research, data granularity, machine learning, Molecular Medicine, Genetics, General Medicine, :Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC], Cancer research, Machine learning, Artificial intelligence, Data granularity, Intel.ligència artificial, Computer science, Genome scale, Granularity, Interoperability, Data type, Small data, Discriminative model, Big data, business.industry, business, Crosstalk, Artificial intelligence, Cancer research, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, lcsh:RC254-282
Related Organizations
Funded by
individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology
  • Funder: European Commission (EC)
  • Project Code: 826121
  • Funding stream: H2020 | RIA
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