PhenoKG: Knowledge Graph-Driven Gene Discovery and Patient Insights from Phenotypes Alone
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911007252676608 |
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| author | Zaripova, Kamilia Özsoy, Ege Navab, Nassir Farshad, Azade |
| author_facet | Zaripova, Kamilia Özsoy, Ege Navab, Nassir Farshad, Azade |
| contents | Identifying causative genes from patient phenotypes remains a significant challenge in precision medicine, with important implications for the diagnosis and treatment of genetic disorders. We propose a novel graph-based approach for predicting causative genes from patient phenotypes, with or without an available list of candidate genes, by integrating a rare disease knowledge graph (KG). Our model, combining graph neural networks and transformers, achieves substantial improvements over the current state-of-the-art. On the real-world MyGene2 dataset, it attains a mean reciprocal rank (MRR) of 24.64\% and nDCG@100 of 33.64\%, surpassing the best baseline (SHEPHERD) at 19.02\% MRR and 30.54\% nDCG@100. We perform extensive ablation studies to validate the contribution of each model component. Notably, the approach generalizes to cases where only phenotypic data are available, addressing key challenges in clinical decision support when genomic information is incomplete. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13119 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PhenoKG: Knowledge Graph-Driven Gene Discovery and Patient Insights from Phenotypes Alone Zaripova, Kamilia Özsoy, Ege Navab, Nassir Farshad, Azade Machine Learning Artificial Intelligence Neural and Evolutionary Computing Genomics Quantitative Methods 92C50, 68T05 I.2.6; H.2.8; J.3 Identifying causative genes from patient phenotypes remains a significant challenge in precision medicine, with important implications for the diagnosis and treatment of genetic disorders. We propose a novel graph-based approach for predicting causative genes from patient phenotypes, with or without an available list of candidate genes, by integrating a rare disease knowledge graph (KG). Our model, combining graph neural networks and transformers, achieves substantial improvements over the current state-of-the-art. On the real-world MyGene2 dataset, it attains a mean reciprocal rank (MRR) of 24.64\% and nDCG@100 of 33.64\%, surpassing the best baseline (SHEPHERD) at 19.02\% MRR and 30.54\% nDCG@100. We perform extensive ablation studies to validate the contribution of each model component. Notably, the approach generalizes to cases where only phenotypic data are available, addressing key challenges in clinical decision support when genomic information is incomplete. |
| title | PhenoKG: Knowledge Graph-Driven Gene Discovery and Patient Insights from Phenotypes Alone |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing Genomics Quantitative Methods 92C50, 68T05 I.2.6; H.2.8; J.3 |
| url | https://arxiv.org/abs/2506.13119 |