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Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology.

Authors
  • Del Giudice, Marco1, 2
  • Peirone, Serena1, 3
  • Perrone, Sarah1, 4
  • Priante, Francesca1, 4
  • Varese, Fabiola1, 5
  • Tirtei, Elisa6
  • Fagioli, Franca6, 7
  • Cereda, Matteo1, 2
  • 1 Cancer Genomics and Bioinformatics Unit, IIGM-Italian Institute for Genomic Medicine, c/o IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy. , (Italy)
  • 2 Candiolo Cancer Institute, FPO-IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy. , (Italy)
  • 3 Department of Physics and INFN, Università degli Studi di Torino, via P.Giuria 1, 10125 Turin, Italy. , (Italy)
  • 4 Department of Physics, Università degli Studi di Torino, via P.Giuria 1, 10125 Turin, Italy. , (Italy)
  • 5 Department of Life Science and System Biology, Università degli Studi di Torino, via Accademia Albertina 13, 10123 Turin, Italy. , (Italy)
  • 6 Paediatric Onco-Haematology Division, Regina Margherita Children's Hospital, City of Health and Science of Turin, 10126 Turin, Italy. , (Italy)
  • 7 Department of Public Health and Paediatric Sciences, University of Torino, 10124 Turin, Italy. , (Italy)
Type
Published Article
Journal
International Journal of Molecular Sciences
Publisher
MDPI AG
Publication Date
Apr 27, 2021
Volume
22
Issue
9
Identifiers
DOI: 10.3390/ijms22094563
PMID: 33925407
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.

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