Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach

Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach

Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting canc...

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Bibliographic Details
Main Authors: Gabriele Malagoli, Filippo Valle, Emmanuel Barillot, Michele Caselle, Loredana Martignetti
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Cancers
Subjects:
topic modeling
hierarchical stochastic block modeling
single-cell RNA-seq
long non-coding RNAs
breast cancer
Online Access:https://www.mdpi.com/2072-6694/16/7/1350

Internet

https://www.mdpi.com/2072-6694/16/7/1350

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