GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

  • Hung I Harry Chen (Contributor)
  • Yu Chiao Chiu (Contributor)
  • Tinghe Zhang (Contributor)
  • Songyao Zhang (Contributor)
  • Yufei Huang (Contributor)
  • Yidong Chen (Contributor)



Abstract Background Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologistsâ capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter-gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. Results In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersetsâ ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Conclusions Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.
Date made available2018

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