greenelab/tybalt
Training and evaluating a variational autoencoder for pan-cancer gene expression data
This project helps cancer researchers and computational biologists analyze complex gene expression data from tumors. It takes raw RNA-seq gene expression measurements across various cancer types and identifies underlying patterns, revealing distinct cancer signatures and states. The output is a compressed, interpretable representation of these genetic patterns, allowing users to understand the heterogeneity of cancer.
174 stars. No commits in the last 6 months.
Use this if you need to uncover hidden biological insights and reduce the complexity of high-dimensional pan-cancer gene expression data to identify disease subtypes or pathways.
Not ideal if your primary goal is to predict patient outcomes directly or analyze genomic data types other than gene expression.
Stars
174
Forks
64
Language
HTML
License
BSD-3-Clause
Last pushed
Jan 31, 2019
Commits (30d)
0
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