PAUSE: principled feature attribution for unsupervised gene expression analysis

PAUSE: principled feature attribution for unsupervised gene expression analysis

Abstract As interest in using unsupervised deep learning models to analyze gene expression data has grown, an increasing number of methods have been developed to make these models more interpretable. These methods can be separated into two groups: post hoc analyses of black box models through featur...

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Bibliographic Details
Main Authors: Joseph D. Janizek, Anna Spiro, Safiye Celik, Ben W. Blue, John C. Russell, Ting-I Lee, Matt Kaeberlin, Su-In Lee
Format: Article
Language:English
Published: BMC 2023-04-01
Series:Genome Biology
Subjects:
Transcriptomics
Gene expression
Deep learning
Explainable AI
Unsupervised learning
Feature attribution
Online Access:https://doi.org/10.1186/s13059-023-02901-4

Internet

https://doi.org/10.1186/s13059-023-02901-4

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