Learning a Latent Space of Highly Multidimensional Cancer Data.
Ontology highlight
ABSTRACT:
SUBMITTER: Kompa B
PROVIDER: S-EPMC6934353 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Learning a Latent Space of Highly Multidimensional Cancer Data.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 20200101
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, ...[more]
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