Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB

Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB

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 identif...

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Bibliographic Details
Main Authors: Katia Pane, Mario Zanfardino, Anna Maria Grimaldi, Gustavo Baldassarre, Marco Salvatore, Mariarosaria Incoronato, Monica Franzese
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Biomedicines
Subjects:
microRNAs
TCGA
machine learning
female-specific cancers
estrogen-dependent cancer
<i>ERBB</i> family
Online Access:https://www.mdpi.com/2227-9059/10/6/1306

Internet

https://www.mdpi.com/2227-9059/10/6/1306

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