Identification of cancer risk groups through multi-omics integration using autoencoder and tensor analysis

HIGHLIGHTS

• What: The authors propose a non-linear multi-omics method that considers the non-linear relationships between features in the assays.. The authors investigate whether the patients can be stratified into risk groups for Glioma cancer using the latent features from the multi-omics genomics data. In this experiment, the authors first applied t-SNE on the latent variables extracted from the proposed method to identify clusters of samples with similar latent variable values. While the original data may not be fully recoverable from the CP decomposition of the compressed data, the authors focused on obtaining a . . .
  

   

  

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