# Unveiling new disease, pathway, and gene associations via multi-scale neural networks

## TL;DR

This study introduces multi-scale neural networks inspired by cellular organization to analyze gene expression data, successfully predicting diagnoses and uncovering novel disease associations, pathways, and gene links.

## Contribution

The paper presents a novel neural network architecture based on cellular multi-scale organization for analyzing gene expression data to discover disease associations.

## Key findings

- Accurately predicts patient diagnoses using the proposed neural networks.
- Identifies and validates new disease-disease, disease-pathway, and disease-gene associations.
- Provides putative explanations for novel associations through literature validation.

## Abstract

Diseases involve complex processes and modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, biological knowledge pertaining to a disease can be derived from a patient cell's profile, improving our diagnosis ability, as well as our grasp of disease risks. This knowledge can be used for drug re-purposing, or by physicians to evaluate a patient's condition and co-morbidity risk. Here, we look at differential gene expression obtained from microarray technology for patients diagnosed with various diseases. Based on this data and cellular multi-scale organization, we aim to uncover disease--disease links, as well as disease-gene and disease--pathways associations. We propose neural networks with structures inspired by the multi-scale organization of a cell. We show that these models are able to correctly predict the diagnosis for the majority of the patients. Through the analysis of the trained models, we predict and validate disease-disease, disease-pathway, and disease-gene associations with comparisons to known interactions and literature search, proposing putative explanations for the novel predictions that come from our study.