{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7A2EJVT5CTVXFFAHYCIMIWCXYZ","short_pith_number":"pith:7A2EJVT5","schema_version":"1.0","canonical_sha256":"f83444d67d14eb729407c090c45857c66b6328dd407da906ec53817b5d7e1c89","source":{"kind":"arxiv","id":"2411.14471","version":1},"attestation_state":"computed","paper":{"title":"Leveraging Gene Expression Data and Explainable Machine Learning for Enhanced Early Detection of Type 2 Diabetes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-bio.GN","authors_text":"Abdullah Al Maruf, Aurora Lithe Roy, Md Kamrul Siam, Nuzhat Noor Islam Prova, Sumaiya Jahan","submitted_at":"2024-11-18T20:24:08Z","abstract_excerpt":"Diabetes, particularly Type 2 diabetes (T2D), poses a substantial global health burden, compounded by its associated complications such as cardiovascular diseases, kidney failure, and vision impairment. Early detection of T2D is critical for improving healthcare outcomes and optimizing resource allocation. In this study, we address the gap in early T2D detection by leveraging machine learning (ML) techniques on gene expression data obtained from T2D patients. Our primary objective was to enhance the accuracy of early T2D detection through advanced ML methodologies and increase the model's trus"},"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":"2411.14471","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.GN","submitted_at":"2024-11-18T20:24:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2665effe0154b0499a1fb98531312da8a144264203bc0205f266f88e8ad0aa80","abstract_canon_sha256":"04204a9e7e221f552cf782473ff998b6267b3eb2f63096cd533bfa9ef4d2c49d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:51.684730Z","signature_b64":"bvtBPauvePpvQz5qKyHwm0TGsgPlbrV2W+IMW4at83W+0DFullLtZ5RPmpjONeEC9JhJOZZlqB9X6SWvlP/JBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f83444d67d14eb729407c090c45857c66b6328dd407da906ec53817b5d7e1c89","last_reissued_at":"2026-07-05T09:38:51.684334Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:51.684334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leveraging Gene Expression Data and Explainable Machine Learning for Enhanced Early Detection of Type 2 Diabetes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-bio.GN","authors_text":"Abdullah Al Maruf, Aurora Lithe Roy, Md Kamrul Siam, Nuzhat Noor Islam Prova, Sumaiya Jahan","submitted_at":"2024-11-18T20:24:08Z","abstract_excerpt":"Diabetes, particularly Type 2 diabetes (T2D), poses a substantial global health burden, compounded by its associated complications such as cardiovascular diseases, kidney failure, and vision impairment. Early detection of T2D is critical for improving healthcare outcomes and optimizing resource allocation. In this study, we address the gap in early T2D detection by leveraging machine learning (ML) techniques on gene expression data obtained from T2D patients. Our primary objective was to enhance the accuracy of early T2D detection through advanced ML methodologies and increase the model's trus"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14471","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/2411.14471/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":"2411.14471","created_at":"2026-07-05T09:38:51.684391+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.14471v1","created_at":"2026-07-05T09:38:51.684391+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14471","created_at":"2026-07-05T09:38:51.684391+00:00"},{"alias_kind":"pith_short_12","alias_value":"7A2EJVT5CTVX","created_at":"2026-07-05T09:38:51.684391+00:00"},{"alias_kind":"pith_short_16","alias_value":"7A2EJVT5CTVXFFAH","created_at":"2026-07-05T09:38:51.684391+00:00"},{"alias_kind":"pith_short_8","alias_value":"7A2EJVT5","created_at":"2026-07-05T09:38:51.684391+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.03744","citing_title":"Do We Need Pre-Processing for Deep Learning Based Ultrasound Shear Wave Elastography?","ref_index":2024,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7A2EJVT5CTVXFFAHYCIMIWCXYZ","json":"https://pith.science/pith/7A2EJVT5CTVXFFAHYCIMIWCXYZ.json","graph_json":"https://pith.science/api/pith-number/7A2EJVT5CTVXFFAHYCIMIWCXYZ/graph.json","events_json":"https://pith.science/api/pith-number/7A2EJVT5CTVXFFAHYCIMIWCXYZ/events.json","paper":"https://pith.science/paper/7A2EJVT5"},"agent_actions":{"view_html":"https://pith.science/pith/7A2EJVT5CTVXFFAHYCIMIWCXYZ","download_json":"https://pith.science/pith/7A2EJVT5CTVXFFAHYCIMIWCXYZ.json","view_paper":"https://pith.science/paper/7A2EJVT5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.14471&json=true","fetch_graph":"https://pith.science/api/pith-number/7A2EJVT5CTVXFFAHYCIMIWCXYZ/graph.json","fetch_events":"https://pith.science/api/pith-number/7A2EJVT5CTVXFFAHYCIMIWCXYZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7A2EJVT5CTVXFFAHYCIMIWCXYZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7A2EJVT5CTVXFFAHYCIMIWCXYZ/action/storage_attestation","attest_author":"https://pith.science/pith/7A2EJVT5CTVXFFAHYCIMIWCXYZ/action/author_attestation","sign_citation":"https://pith.science/pith/7A2EJVT5CTVXFFAHYCIMIWCXYZ/action/citation_signature","submit_replication":"https://pith.science/pith/7A2EJVT5CTVXFFAHYCIMIWCXYZ/action/replication_record"}},"created_at":"2026-07-05T09:38:51.684391+00:00","updated_at":"2026-07-05T09:38:51.684391+00:00"}