Artificial intelligence for precision oncology: beyond patient stratification.


Journal

NPJ precision oncology
ISSN: 2397-768X
Titre abrégé: NPJ Precis Oncol
Pays: England
ID NLM: 101708166

Informations de publication

Date de publication:
2019
Historique:
received: 05 11 2018
accepted: 22 01 2019
entrez: 2 3 2019
pubmed: 2 3 2019
medline: 2 3 2019
Statut: epublish

Résumé

The data-driven identification of disease states and treatment options is a crucial challenge for precision oncology. Artificial intelligence (AI) offers unique opportunities for enhancing such predictive capabilities in the lab and the clinic. AI, including its best-known branch of research, machine learning, has significant potential to enable precision oncology well beyond relatively well-known pattern recognition applications, such as the supervised classification of single-source omics or imaging datasets. This perspective highlights key advances and challenges in that direction. Furthermore, it argues that AI's scope and depth of research need to be expanded to achieve ground-breaking progress in precision oncology.

Identifiants

pubmed: 30820462
doi: 10.1038/s41698-019-0078-1
pii: 78
pmc: PMC6389974
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

6

Déclaration de conflit d'intérêts

The author declares no competing interests.

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Auteurs

Francisco Azuaje (F)

1Bioinformatics and Modelling Research Group, Department of Oncology, Luxembourg Institute of Health (LIH), L-1445 Strassen, Luxembourg.
2Present Address: Computational Biomedicine Research Group, Center for Quantitative Biology, Luxembourg Institute of Health (LIH), L-1445 Strassen, Luxembourg.

Classifications MeSH