Research on Disease-Associated Gene Identification and Validation Based on Biological Big Data

Abstract

This paper explores methods for disease-associated gene identification and validation based on biological big data. Through various
approaches including gene expression data analysis, functional annotation and enrichment analysis, protein-protein interaction network analysis, and genomic variation analysis, disease-related genes can be precisely identified. Meanwhile, clinical translation research of candidate
genes is conducted through biomarker validation and personalized medicine practices. Case studies demonstrate that this method can effectively identify genes associated with diabetes and non-union fractures, and verify their potential therapeutic value through multi-dimensional
validation, providing strong support for disease mechanism research and personalized medicine.