Gene-disease association using Machine Learning
| dc.contributor.author | Amina, Boufissiou | |
| dc.contributor.author | Supervisor: Lamri, Sayad | |
| dc.date.accessioned | 2025-07-08T12:36:51Z | |
| dc.date.available | 2025-07-08T12:36:51Z | |
| dc.date.issued | 2025-06-15 | |
| dc.description.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 | |
| dc.identifier.uri | https://repository.univ-msila.dz/handle/123456789/46815 | |
| dc.language.iso | en | |
| dc.publisher | Mohamed Boudiaf University of M'sila | |
| dc.subject | Gene-Disease Association | |
| dc.subject | Machine Learning | |
| dc.subject | Genomics | |
| dc.subject | Asthma | |
| dc.subject | Cardiomyopathy | |
| dc.subject | XGBoost | |
| dc.subject | MLP | |
| dc.subject | Clinical Decision | |
| dc.subject | Explainable AI | |
| dc.title | Gene-disease association using Machine Learning | |
| dc.type | Thesis |