Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification

Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification

Advances in high-throughput technologies encourage the generation of large amounts of multiomics data to investigate complex diseases, including breast cancer. Given that the aetiologies of such diseases extend beyond a single biological entity, and that essential biological information can be carri...

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Bibliographic Details
Main Authors: Jelmar Quist, Lawson Taylor, Johan Staaf, Anita Grigoriadis
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Cancers
Subjects:
breast cancer
random forest
machine learning
integrative analysis
DNA damage repair
Online Access:https://www.mdpi.com/2072-6694/13/5/991

Internet

https://www.mdpi.com/2072-6694/13/5/991

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