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Review

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

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
2
Candiolo Cancer Institute, FPO—IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy
3
Department of Physics and INFN, Università degli Studi di Torino, via P.Giuria 1, 10125 Turin, Italy
4
Department of Physics, Università degli Studi di Torino, via P.Giuria 1, 10125 Turin, Italy
5
Department of Life Science and System Biology, Università degli Studi di Torino, via Accademia Albertina 13, 10123 Turin, Italy
6
Paediatric Onco-Haematology Division, Regina Margherita Children’s Hospital, City of Health and Science of Turin, 10126 Turin, Italy
7
Department of Public Health and Paediatric Sciences, University of Torino, 10124 Turin, Italy
*
Author to whom correspondence should be addressed.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
Academic Editor: Jung Hun Oh
Int. J. Mol. Sci. 2021, 22(9), 4563; https://doi.org/10.3390/ijms22094563
Received: 20 March 2021 / Revised: 21 April 2021 / Accepted: 23 April 2021 / Published: 27 April 2021
(This article belongs to the Special Issue Deep Learning and Machine Learning in Bioinformatics)
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. View Full-Text
Keywords: artificial intelligence; RNA sequencing; cancer heterogeneity artificial intelligence; RNA sequencing; cancer heterogeneity
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MDPI and ACS Style

Del Giudice, M.; Peirone, S.; Perrone, S.; Priante, F.; Varese, F.; Tirtei, E.; Fagioli, F.; Cereda, M. Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology. Int. J. Mol. Sci. 2021, 22, 4563. https://doi.org/10.3390/ijms22094563

AMA Style

Del Giudice M, Peirone S, Perrone S, Priante F, Varese F, Tirtei E, Fagioli F, Cereda M. Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology. International Journal of Molecular Sciences. 2021; 22(9):4563. https://doi.org/10.3390/ijms22094563

Chicago/Turabian Style

Del Giudice, Marco, Serena Peirone, Sarah Perrone, Francesca Priante, Fabiola Varese, Elisa Tirtei, Franca Fagioli, and Matteo Cereda. 2021. "Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology" International Journal of Molecular Sciences 22, no. 9: 4563. https://doi.org/10.3390/ijms22094563

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