| Comparison: adaptation maximum delta score > 0.5 (Legacy Ensemble N > 1) Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ Table 3 row 'OpenSpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML Retrieved: 2026-10-11T21:37:04Z | not individually reviewed No individual claim review recorded independent paper Audit detailsField: attributes.comparison.adaptation Source artifact SHA-256: dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176 Hash scope: Hash scope not separately documented; inspect source record Inspected artifact |
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| Comparison: aggregation Whole dataset Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ Table 3 row 'OpenSpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML Retrieved: 2026-10-11T21:37:04Z | not individually reviewed No individual claim review recorded independent paper Audit detailsField: attributes.comparison.aggregation Source artifact SHA-256: dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176 Hash scope: Hash scope not separately documented; inspect source record Inspected artifact |
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| Comparison: budget Not reported Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ Table 3 row 'OpenSpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML Retrieved: 2026-10-11T21:37:04Z | missing or unspecified No individual claim review recorded independent paper Audit detailsField: attributes.comparison.budget Source artifact SHA-256: dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176 Hash scope: Hash scope not separately documented; inspect source record Inspected artifact |
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| Comparison: dataset version As assembled by Fortier et al. 2026 Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ Table 3 row 'OpenSpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML Retrieved: 2026-10-11T21:37:04Z | not individually reviewed No individual claim review recorded independent paper Audit detailsField: attributes.comparison.dataset_version Source artifact SHA-256: dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176 Hash scope: Hash scope not separately documented; inspect source record Inspected artifact |
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| Comparison: inputs Variant with genomic sequence context (VCF) Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ Table 3 row 'OpenSpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML Retrieved: 2026-10-11T21:37:04Z | not individually reviewed No individual claim review recorded independent paper Audit detailsField: attributes.comparison.inputs Source artifact SHA-256: dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176 Hash scope: Hash scope not separately documented; inspect source record Inspected artifact |
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| Comparison: metric implementation Sensitivity, specificity, F1 = 2TP/(2TP+FP+FN), balanced accuracy = mean of sensitivity and specificity (Equations 1 to 4) Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ Table 3 row 'OpenSpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML Retrieved: 2026-10-11T21:37:04Z | not individually reviewed No individual claim review recorded independent paper Audit detailsField: attributes.comparison.metric_implementation Source artifact SHA-256: dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176 Hash scope: Hash scope not separately documented; inspect source record Inspected artifact |
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| Comparison: population 4,616 experimentally characterised variants from 227 genes (1,924 spliceogenic by RT-PCR, Sanger sequencing of RT-PCR products or minigene assays) plus 95,000 common control variants (MAF > 5%) from Leman et al.; 15 variants excluded on conversion to GRCh38, leaving 99,601; about 2% spliceogenic Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ Table 3 row 'OpenSpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML Retrieved: 2026-10-11T21:37:04Z | not individually reviewed No individual claim review recorded independent paper Audit detailsField: attributes.comparison.population Source artifact SHA-256: dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176 Hash scope: Hash scope not separately documented; inspect source record Inspected artifact |
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| Comparison: protocol id rna-splicing-20261011-fortier2026-protocol-spip Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ Table 3 row 'OpenSpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML Retrieved: 2026-10-11T21:37:04Z | not individually reviewed No individual claim review recorded independent paper Audit detailsField: attributes.comparison.protocol_id Source artifact SHA-256: dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176 Hash scope: Hash scope not separately documented; inspect source record Inspected artifact |
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| Comparison: split Whole set Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ Table 3 row 'OpenSpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML Retrieved: 2026-10-11T21:37:04Z | not individually reviewed No individual claim review recorded independent paper Audit detailsField: attributes.comparison.split Source artifact SHA-256: dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176 Hash scope: Hash scope not separately documented; inspect source record Inspected artifact |
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| Origin independent_paper Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Original source ↗ Table 3 row 'OpenSpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML Retrieved: 2026-10-11T21:37:04Z | not individually reviewed No individual claim review recorded independent paper Audit detailsField: attributes.origin Source artifact SHA-256: dffe74cee7782cbdfdbc9423cf00f0a846b3d720b90b10e9732d6f768c485176 Hash scope: Hash scope not separately documented; inspect source record Inspected artifact |
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