ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2020
Learning a Latent Space of Highly Multidimensional Cancer Data.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
We introduce a Unified Disentanglement Network (UFDN) trained on The Cancer Genome Atlas (TCGA), which we refer to as UFDN-TCGA. We demonstrate that UFDN-TCGA learns a biologically relevant, low-dimensional latent space of high-dimensional gene expression data by applying our network to two classification tasks of cancer status and cancer type. UFDN-TCGA performs comparably to random forest methods. The UFDN allows for continuous, partial interpolation between distinct cancer types. Furthermore, we perform an analysis of differentially expressed genes between skin cutaneous melanoma (SKCM) samples and the same samples interpolated into glioblastoma (GBM). We demonstrate that our interpolations consist of relevant metagenes that recapitulate known glioblastoma mechanisms.
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31797612PMC6934353What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.