Back to search

Article

Deep Learning-Based Molecular Subtyping Identifies Three Biologically Distinct Ovarian Cancer Subtypes with Potential Therapeutic Implications

2026-05-14

Abstract excerpt

<title>Abstract</title> <p>Ovarian cancer exhibits pronounced molecular heterogeneity that complicates therapeutic stratification and patient management. Here we present a comprehensive computational framework integrating variational autoencoders (VAE), probabilistic Gaussian Mixture Model (GMM) clustering, and rigorous consensus stability assessment to deconvolute transcriptomic heterogeneity in 421 high-grade s...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
1c547c2e-6138-5c4e-ab46-94d60bc5a5c2
DOI
10.21203/rs.3.rs-9693642/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Deep Learning-Based Molecular Subtyping Identifies Three Biologically Distinct Ovarian Cancer Subtypes with Potential Therapeutic ImplicationsDOI 10.21203/rs.3.rs-9693642/v1
Select a neighboring publication to make it the new centre.