Gene-disease association using Machine Learning
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Date
2025-06-15
Journal Title
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Publisher
Mohamed Boudiaf University of M'sila
Abstract
This thesis examines how machine learning can be used to forecast gene-disease associations,
focusing on asthma and cardiomyopathy. Traditional genomic approaches often struggle with
complex, high-dimensional data, particularly in non-coding regions. To address this, we
developed a predictive framework using models such as XGBoost and MLP, aiming for both
accuracy and interpretability. Our results show how machine learning (ML) might enhance
early diagnosis and aid in clinical decision-making through a multi-disease prediction model,
achieving high accuracy for both conditions (79% for asthma and 96% for cardiomyopathy).
Future directions include multi-omics integration, clinical validation, and the application of
explainable AI to improve trust and usability
Description
Keywords
Gene-Disease Association, Machine Learning, Genomics, Asthma, Cardiomyopathy, XGBoost, MLP, Clinical Decision, Explainable AI