Hybrid deep learning models for text-based identification of gene-disease associations.

ABSTRACT:

Introduction

Identifying gene-disease associations is crucial for advancing medical research and improving clinical outcomes. Nevertheless, the rapid expansion of biomedical literature poses significant obstacles to extracting meaningful relationships from extensive text collections.

Methods

This study uses deep learning techniques to automate this process, using publicly available datasets (EU-ADR, GAD, and SNPPhenA) to classify these associations accurately. Each dataset underwent rigorous pre-processing, including entity identification and preparation, word embedding using pre-trained Word2Vec and fastText models, and position embedding to capture semantic and contextual relationships within the text. In this research, three deep learning-based hybrid models have been imp

SUBMITTER: Fadhil Jumaa N 

PROVIDER: S-EPMC12319213 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature