Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB.
ERBB family
TCGA
bioinformatics
estrogen-dependent cancer
female-specific cancers
machine learning
microRNAs
Journal
Biomedicines
ISSN: 2227-9059
Titre abrégé: Biomedicines
Pays: Switzerland
ID NLM: 101691304
Informations de publication
Date de publication:
02 Jun 2022
02 Jun 2022
Historique:
received:
14
04
2022
revised:
25
05
2022
accepted:
29
05
2022
entrez:
24
6
2022
pubmed:
25
6
2022
medline:
25
6
2022
Statut:
epublish
Résumé
Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the
Identifiants
pubmed: 35740327
pii: biomedicines10061306
doi: 10.3390/biomedicines10061306
pmc: PMC9219956
pii:
doi:
Types de publication
Journal Article
Langues
eng
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