{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WPTKR5G6OMDXKBCLBFI2GRQJXI","short_pith_number":"pith:WPTKR5G6","schema_version":"1.0","canonical_sha256":"b3e6a8f4de730775044b0951a34609ba2785dd7a46f505294f95f909823d5459","source":{"kind":"arxiv","id":"2505.10711","version":1},"attestation_state":"computed","paper":{"title":"GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Giovanni Stracquadanio, Sebesty\\'en Kamp, T. Ian Simpson","submitted_at":"2025-05-15T21:14:30Z","abstract_excerpt":"We present GNN-Suite, a robust modular framework for constructing and benchmarking Graph Neural Network (GNN) architectures in computational biology. GNN-Suite standardises experimentation and reproducibility using the Nextflow workflow to evaluate GNN performance. We demonstrate its utility in identifying cancer-driver genes by constructing molecular networks from protein-protein interaction (PPI) data from STRING and BioGRID and annotating nodes with features from the PCAWG, PID, and COSMIC-CGC repositories.\n  Our design enables fair comparisons among diverse GNN architectures including GAT,"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.10711","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-15T21:14:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"06e676b0d623ea638aef8484b3927facb604b1203d1f12ca61b8202135b9c1c3","abstract_canon_sha256":"cda86fe47e7c95706e81667360bdd9e1e8b04b02af3e2ba53cb09dd67a577f72"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:04.158445Z","signature_b64":"Oqa8GFfsjsoL6pnVrV5B7uFeGwEn8mTwF4hdgyMJyLiNbbTnaBB0gUMPWuDA/DNkhaekhwV5t31dFtyOY3gJDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3e6a8f4de730775044b0951a34609ba2785dd7a46f505294f95f909823d5459","last_reissued_at":"2026-07-05T11:04:04.157968Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:04.157968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Giovanni Stracquadanio, Sebesty\\'en Kamp, T. Ian Simpson","submitted_at":"2025-05-15T21:14:30Z","abstract_excerpt":"We present GNN-Suite, a robust modular framework for constructing and benchmarking Graph Neural Network (GNN) architectures in computational biology. GNN-Suite standardises experimentation and reproducibility using the Nextflow workflow to evaluate GNN performance. We demonstrate its utility in identifying cancer-driver genes by constructing molecular networks from protein-protein interaction (PPI) data from STRING and BioGRID and annotating nodes with features from the PCAWG, PID, and COSMIC-CGC repositories.\n  Our design enables fair comparisons among diverse GNN architectures including GAT,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10711","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.10711/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.10711","created_at":"2026-07-05T11:04:04.158023+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.10711v1","created_at":"2026-07-05T11:04:04.158023+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10711","created_at":"2026-07-05T11:04:04.158023+00:00"},{"alias_kind":"pith_short_12","alias_value":"WPTKR5G6OMDX","created_at":"2026-07-05T11:04:04.158023+00:00"},{"alias_kind":"pith_short_16","alias_value":"WPTKR5G6OMDXKBCL","created_at":"2026-07-05T11:04:04.158023+00:00"},{"alias_kind":"pith_short_8","alias_value":"WPTKR5G6","created_at":"2026-07-05T11:04:04.158023+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08659","citing_title":"EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WPTKR5G6OMDXKBCLBFI2GRQJXI","json":"https://pith.science/pith/WPTKR5G6OMDXKBCLBFI2GRQJXI.json","graph_json":"https://pith.science/api/pith-number/WPTKR5G6OMDXKBCLBFI2GRQJXI/graph.json","events_json":"https://pith.science/api/pith-number/WPTKR5G6OMDXKBCLBFI2GRQJXI/events.json","paper":"https://pith.science/paper/WPTKR5G6"},"agent_actions":{"view_html":"https://pith.science/pith/WPTKR5G6OMDXKBCLBFI2GRQJXI","download_json":"https://pith.science/pith/WPTKR5G6OMDXKBCLBFI2GRQJXI.json","view_paper":"https://pith.science/paper/WPTKR5G6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.10711&json=true","fetch_graph":"https://pith.science/api/pith-number/WPTKR5G6OMDXKBCLBFI2GRQJXI/graph.json","fetch_events":"https://pith.science/api/pith-number/WPTKR5G6OMDXKBCLBFI2GRQJXI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WPTKR5G6OMDXKBCLBFI2GRQJXI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WPTKR5G6OMDXKBCLBFI2GRQJXI/action/storage_attestation","attest_author":"https://pith.science/pith/WPTKR5G6OMDXKBCLBFI2GRQJXI/action/author_attestation","sign_citation":"https://pith.science/pith/WPTKR5G6OMDXKBCLBFI2GRQJXI/action/citation_signature","submit_replication":"https://pith.science/pith/WPTKR5G6OMDXKBCLBFI2GRQJXI/action/replication_record"}},"created_at":"2026-07-05T11:04:04.158023+00:00","updated_at":"2026-07-05T11:04:04.158023+00:00"}