Recommender Systems in Bioinformatics
| dc.contributor.author | Harrouche, Oussama | |
| dc.date.accessioned | 2026-11-02T13:25:25Z | |
| dc.date.available | 2026-11-02T13:25:25Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | The rapid growth of biological data that is paired with the high cost, time consumption, and failure rates associated with traditional drug discovery pipelines have intensified the need for computational approaches capable of efficiently predicting intermolecular interactions and accelerating therapeutic development. Bioinformatics, coupled with machine learning techniques and recommender system paradigms, has emerged as a powerful framework for modeling complex biological relationships such as drug-target and protein-protein interactions, and drug-disease associations. However, these tasks are challenged by data sparsity, high dimensionality, heterogeneity of biological entities, and the presence of orphan proteins or drugs with limited to none interaction knowledge. | |
| dc.format | ||
| dc.identifier.uri | https://dspace.univ-annaba.dz//handle/123456789/4896 | |
| dc.language.iso | English | |
| dc.publisher | Université Badji Mokhtar Annaba | |
| dc.subject | Bioinformatics; drug discovery; deep learning | |
| dc.title | Recommender Systems in Bioinformatics | |
| dc.type | Thesis |