1. Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology
- Author
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Sarah Perrone, Francesca Priante, Fabiola Varese, Elisa Tirtei, Serena Peirone, Franca Fagioli, Matteo Cereda, and Marco Del Giudice
- Subjects
0301 basic medicine ,Computer science ,QH301-705.5 ,Sequencing data ,Genomics ,Review ,cancer heterogeneity ,Catalysis ,Inorganic Chemistry ,03 medical and health sciences ,0302 clinical medicine ,Neoplasms ,Biomarkers, Tumor ,Tumor Microenvironment ,Transcriptome Profiles ,Humans ,Physical and Theoretical Chemistry ,Biology (General) ,Precision Medicine ,Molecular Biology ,QD1-999 ,Spectroscopy ,business.industry ,Human intelligence ,Sequence Analysis, RNA ,Organic Chemistry ,RNA sequencing ,General Medicine ,Precision medicine ,artificial intelligence ,Prognosis ,Rare cancer ,Computer Science Applications ,Patient management ,Chemistry ,030104 developmental biology ,ComputingMethodologies_PATTERNRECOGNITION ,Precision oncology ,030220 oncology & carcinogenesis ,Artificial intelligence ,Single-Cell Analysis ,business ,Algorithms - 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.
- Published
- 2021