Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.
cancer
machine learning
multi-omics
oncology
systematic review
tools
Journal
Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867
Informations de publication
Date de publication:
2020
2020
Historique:
received:
30
01
2020
accepted:
26
05
2020
entrez:
23
7
2020
pubmed:
23
7
2020
medline:
23
7
2020
Statut:
epublish
Résumé
In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.
Identifiants
pubmed: 32695678
doi: 10.3389/fonc.2020.01030
pmc: PMC7338582
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
1030Subventions
Organisme : Medical Research Council
ID : MR/N00583X/1
Pays : United Kingdom
Informations de copyright
Copyright © 2020 Nicora, Vitali, Dagliati, Geifman and Bellazzi.
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