Generated network illustrating the pro-groups and their associated features that are identified by 3Mont for differentiating among the BRCA molecular subtypes (HR

<p> + /HR- cases). The network visualizes associations between mRNAs, CpG IDs, miRNAs within the pro-groups. The node size represents the scaled average score of each associated feature within the top 10 pro-groups. The scores are obtained from the Average scores column of <a href="htt...

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Bibliographic Details
Main Author:	Miray Unlu Yazici (14792680) (author)
Other Authors:	J. S. Marron (11303970) (author), Burcu Bakir-Gungor (199380) (author), Fei Zou (188420) (author), Malik Yousef (87355) (author)
Published:	2025
Subjects:	Pharmacology Biotechnology Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified progesterone hormone receptors improving survival time hormone receptor negative distinct molecular profiles developing effective strategies tool allows users better cancer sub like brca sub distinguishing brca sub ulti -< b utilizing ml algorithms omics integrative tool groups facilitates understanding div >< p address diverse types n </ b 3mont generates networks </ b omics data generated networks breast cancer 3mont tool 3mint tool type stratification supporting materials significant 20 promising biomarkers prominent biomarkers process achieves predicting prognosis machine learning lack estrogen integrates various heterogeneous disease grouping function feature numbers especially basal conventional methods constructed based concerted actions collective behavior biomarkers within biological groups assigning scores 3mont ),
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Generated network illustrating the pro-groups and their associated features that are identified by 3Mont for differentiating among the BRCA molecular subtypes (HR

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