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

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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