Deep gate information bottleneck-based prediction model for complex disease-related micro-ribonucleic acids via heterogeneous biological networks

Abstract

Accurate prediction of potential association patterns is becoming a routine and essential method for detecting, analyzing, and controlling diseases. However, the complexity of biological networks poses significant challenges to computational methods between micro-ribonucleic acids and diseases. In this work, we propose a flexible multi-view gate information bottleneck-driven prediction model for complex disease-related micro-ribonucleic acid prediction. Our proposed model can reduce noise and redundant information from complex biological networks between different scale representations via the information bottleneck mechanism, and then enhances the robustness and generalization performance. Unlike other computational methods, we design the gate variational information bottleneck via a shrinking and an enlarging gate mechanism for multi-view different-order feature learning. The gate variational information bottleneck fuses the shared similarity and the view-specific embedding to obtain discriminative representation, and then eliminates redundant information and enhances task-relevant patterns. Next, the information bottleneck-based model is parameterized by a gate variational autoencoder and the reparameterization trick. Extensive experiments on different genomic datasets show the superior performance of our model compared to baselines, and the proposed model could effectively support the validation of complex disease-related micro-ribonucleic acids. It also shows that the model performance can be effectively improved by multi-view embedding learning and gate variational information bottleneck.