Data integration and mechanistic modelling for breast cancer biology: Current state and future directions.
Breast cancer
Deep learning
Multi-omics modelling
Network biology
Precision oncology
Journal
Current opinion in endocrine and metabolic research
ISSN: 2451-9650
Titre abrégé: Curr Opin Endocr Metab Res
Pays: England
ID NLM: 101722894
Informations de publication
Date de publication:
Jun 2022
Jun 2022
Historique:
entrez:
29
8
2022
pubmed:
30
8
2022
medline:
30
8
2022
Statut:
ppublish
Résumé
Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence, and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for methods to investigate novel treatment options, while taking into account the vast molecular heterogeneity of breast cancer. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics and metabolomics data, enable approaching breast cancer biology at multiple levels of omics interaction networks. Systems biology approaches, including computational inference of 'big data' and mechanistic modelling of specific pathways, are emerging to identify potential novel combinations of breast cancer subtype signatures and more diverse targeted therapies.
Identifiants
pubmed: 36034741
doi: 10.1016/j.coemr.2022.100350
pii: S2451-9650(22)00035-7
pmc: PMC9402443
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
NoneInformations de copyright
© 2022 The Author(s).
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