Bioinformatics Enhances Disease Gene Identification: A Comparative Evaluation of Machine Learning Models
Keywords:
Bioinformatics, machine learning, disease gene identificationAbstract
High-throughput genomic technologies have revolutionized biological research, enabling large-scale DNA and RNA data generation. Identifying disease-associated genes from complex genomic datasets remains challenging using traditional statistical approaches. This study evaluates the performance of three machine learning models—Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN)—in predicting disease-gene associations. Using benchmark genomic datasets, the results demonstrate superior predictive accuracy for the DNN model, followed by RF and SVM. These findings suggest that advanced bioinformatics methods significantly improve disease gene identification and have implications for precision medicine.
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