Latent disease similarities and therapeutic repurposing possibilities uncovered by multi-modal generative topic modeling of human diseases

Abstract

Human diseases are multifactorial - hence it is important to characterize diseases on the basis of multiple disease-omics. However, the capability of the existing methods is largely limited to classifying diseases based on a single type or a few closely related omics data. Herein, we report a topic model framework that allows for characterizing diseases according to their multiple omics data. We also show that this method can be utilized to predict potential biomarkers and/or therapeutic targets. In this study, we illustrate a computational concept of this augmented topic model and demonstrate its prediction performance by a leave one-disease features out cross-validation scheme. Furthermore, we exploit this method together with human disease tissue/organ-transcriptome data and identify putative biomarkers and/or therapeutic targets across 79 diseases. In conclusion, this method and the prediction framework shown reported herein provide important tools for understanding complex human diseases and also facilitate diagnostic and/or therapeutic development.