| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.445 AUPRC (candidate gene set of 100 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 100, 'AUPR', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.87 AUROC (candidate gene set of 100 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 100, 'ROC AUC', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 58.6% Top-1 accuracy (candidate gene set of 100 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 100, 'Hits@1 (%)', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 78.4% Top-10 accuracy (candidate gene set of 100 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 100, 'Hits@10 (%)', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.618 AUPRC (candidate gene set of 25 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 25, 'AUPR', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.88 AUROC (candidate gene set of 25 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 25, 'ROC AUC', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 65.6% Top-1 accuracy (candidate gene set of 25 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 25, 'Hits@1 (%)', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 82.6% Top-10 accuracy (candidate gene set of 25 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 25, 'Hits@10 (%)', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.771 AUPRC (candidate gene set of 5 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 5, 'AUPR', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.927 AUROC (candidate gene set of 5 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 5, 'ROC AUC', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 97.6% Top-1 accuracy (candidate gene set of 5 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 5, 'Hits@1 (%)', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 100% Top-10 accuracy (candidate gene set of 5 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 5, 'Hits@10 (%)', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.54 AUPRC (candidate gene set of 50 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 50, 'AUPR', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.875 AUROC (candidate gene set of 50 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 50, 'ROC AUC', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 57.2% Top-1 accuracy (candidate gene set of 50 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 50, 'Hits@1 (%)', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 80.2% Top-10 accuracy (candidate gene set of 50 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 50, 'Hits@10 (%)', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.483 AUPRC (candidate gene set of 75 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 75, 'AUPR', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.872 AUROC (candidate gene set of 75 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 75, 'ROC AUC', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 51.6% Top-1 accuracy (candidate gene set of 75 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 75, 'Hits@1 (%)', column 'PAVS' 'Exomiser' |
|---|
| Configuration: Exomiser 12.1.0 'Exomiser score' gene ranking (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 79.4% Top-10 accuracy (candidate gene set of 75 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceExomiser on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 75, 'Hits@10 (%)', column 'PAVS' 'Exomiser' |
|---|
| Configuration: GPT-4 (gpt-4-1106-preview), one-shot chain-of-thought prompt Q4 (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.539 AUPRC (candidate gene set of 100 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGPT-4 One shot on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 100, 'AUPR', column 'PAVS' 'GPT-4' 'One shot' |
|---|
| Configuration: GPT-4 (gpt-4-1106-preview), one-shot chain-of-thought prompt Q4 (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.937 AUROC (candidate gene set of 100 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGPT-4 One shot on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 100, 'ROC AUC', column 'PAVS' 'GPT-4' 'One shot' |
|---|
| Configuration: GPT-4 (gpt-4-1106-preview), one-shot chain-of-thought prompt Q4 (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 57.8% Top-1 accuracy (candidate gene set of 100 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGPT-4 One shot on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 100, 'Hits@1 (%)', column 'PAVS' 'GPT-4' 'One shot' |
|---|
| Configuration: GPT-4 (gpt-4-1106-preview), one-shot chain-of-thought prompt Q4 (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 84.8% Top-10 accuracy (candidate gene set of 100 genes including the causative gene) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGPT-4 One shot on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 100, 'Hits@10 (%)', column 'PAVS' 'GPT-4' 'One shot' |
|---|
| Configuration: GPT-4 (gpt-4-1106-preview), one-shot chain-of-thought prompt Q4 (Kafkas et al. 2025) | Protocol: Ranking the causative gene within synthetic candidate sets of 5 to 100 genes, PAVS (Kafkas et al. 2025 Table 3)
Dataset: PAVS Saudi phenotype-associated variants, 500 genes (Kafkas et al. 2025) | 0.784 AUPRC (candidate gene set of 25 genes including the causative gene) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGPT-4 One shot on PAVS candidate gene sets rare-ranking-20261009-protocol-kafkas2025-pavs-gene-sets Aggregation: Not reported The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients · Table 3 row Size 25, 'AUPR', column 'PAVS' 'GPT-4' 'One shot' |
|---|