{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GKXC7YL5J54NLEA2YEQBFYA5J5","short_pith_number":"pith:GKXC7YL5","schema_version":"1.0","canonical_sha256":"32ae2fe17d4f78d5901ac12012e01d4f73b03194415f0aa91c3789545ba70c13","source":{"kind":"arxiv","id":"2312.08381","version":1},"attestation_state":"computed","paper":{"title":"An Explainable Machine Learning Framework for the Accurate Diagnosis of Ovarian Cancer","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Abdullah Taharat, A.G.M. Fuad Hasan Akanda, Asif Newaz, Md Sakibul Islam","submitted_at":"2023-12-11T16:52:50Z","abstract_excerpt":"Ovarian cancer (OC) is one of the most prevalent types of cancer in women. Early and accurate diagnosis is crucial for the survival of the patients. However, the majority of women are diagnosed in advanced stages due to the lack of effective biomarkers and accurate screening tools. While previous studies sought a common biomarker, our study suggests different biomarkers for the premenopausal and postmenopausal populations. This can provide a new perspective in the search for novel predictors for the effective diagnosis of OC. Lack of explainability is one major limitation of current AI systems"},"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":"2312.08381","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-11T16:52:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"30cdfe3b430999b7be47a95b548d8681528e068c203dc9d9802d00bb25c3a4dc","abstract_canon_sha256":"c971319a26483b6da31c403a8987ecc9e6f1e6b13925f8a531b27373e3fcc07e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:50:08.784480Z","signature_b64":"SeDlhLJPa0oTcEAPbeWrJhjjj8bu6QLgaM/Q/F5g3Gi26mThQjdsn3Bz9bVC/Wt4KUvSR9UFdO9q8uDnfo+dCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"32ae2fe17d4f78d5901ac12012e01d4f73b03194415f0aa91c3789545ba70c13","last_reissued_at":"2026-07-05T08:50:08.784004Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:50:08.784004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Explainable Machine Learning Framework for the Accurate Diagnosis of Ovarian Cancer","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Abdullah Taharat, A.G.M. Fuad Hasan Akanda, Asif Newaz, Md Sakibul Islam","submitted_at":"2023-12-11T16:52:50Z","abstract_excerpt":"Ovarian cancer (OC) is one of the most prevalent types of cancer in women. Early and accurate diagnosis is crucial for the survival of the patients. However, the majority of women are diagnosed in advanced stages due to the lack of effective biomarkers and accurate screening tools. While previous studies sought a common biomarker, our study suggests different biomarkers for the premenopausal and postmenopausal populations. This can provide a new perspective in the search for novel predictors for the effective diagnosis of OC. Lack of explainability is one major limitation of current AI systems"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.08381","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/2312.08381/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":"2312.08381","created_at":"2026-07-05T08:50:08.784068+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.08381v1","created_at":"2026-07-05T08:50:08.784068+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.08381","created_at":"2026-07-05T08:50:08.784068+00:00"},{"alias_kind":"pith_short_12","alias_value":"GKXC7YL5J54N","created_at":"2026-07-05T08:50:08.784068+00:00"},{"alias_kind":"pith_short_16","alias_value":"GKXC7YL5J54NLEA2","created_at":"2026-07-05T08:50:08.784068+00:00"},{"alias_kind":"pith_short_8","alias_value":"GKXC7YL5","created_at":"2026-07-05T08:50:08.784068+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GKXC7YL5J54NLEA2YEQBFYA5J5","json":"https://pith.science/pith/GKXC7YL5J54NLEA2YEQBFYA5J5.json","graph_json":"https://pith.science/api/pith-number/GKXC7YL5J54NLEA2YEQBFYA5J5/graph.json","events_json":"https://pith.science/api/pith-number/GKXC7YL5J54NLEA2YEQBFYA5J5/events.json","paper":"https://pith.science/paper/GKXC7YL5"},"agent_actions":{"view_html":"https://pith.science/pith/GKXC7YL5J54NLEA2YEQBFYA5J5","download_json":"https://pith.science/pith/GKXC7YL5J54NLEA2YEQBFYA5J5.json","view_paper":"https://pith.science/paper/GKXC7YL5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.08381&json=true","fetch_graph":"https://pith.science/api/pith-number/GKXC7YL5J54NLEA2YEQBFYA5J5/graph.json","fetch_events":"https://pith.science/api/pith-number/GKXC7YL5J54NLEA2YEQBFYA5J5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GKXC7YL5J54NLEA2YEQBFYA5J5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GKXC7YL5J54NLEA2YEQBFYA5J5/action/storage_attestation","attest_author":"https://pith.science/pith/GKXC7YL5J54NLEA2YEQBFYA5J5/action/author_attestation","sign_citation":"https://pith.science/pith/GKXC7YL5J54NLEA2YEQBFYA5J5/action/citation_signature","submit_replication":"https://pith.science/pith/GKXC7YL5J54NLEA2YEQBFYA5J5/action/replication_record"}},"created_at":"2026-07-05T08:50:08.784068+00:00","updated_at":"2026-07-05T08:50:08.784068+00:00"}