{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:VJROLVSKDW2NDI5GWWV4MUFGDT","short_pith_number":"pith:VJROLVSK","schema_version":"1.0","canonical_sha256":"aa62e5d64a1db4d1a3a6b5abc650a61ce664926b2d5a261df1721386dbc94e60","source":{"kind":"arxiv","id":"2005.10831","version":1},"attestation_state":"computed","paper":{"title":"Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-bio.QM","authors_text":"Feixiong Cheng, George Karypis, Tengfei Ma, Xiang Song, Xiangxiang Zeng, Xiaoqin Pan, Yadi Zhou, Yuan Hou, Zheng Zhang","submitted_at":"2020-05-21T16:02:29Z","abstract_excerpt":"There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there are currently no proven effective medications against COVID-19. Drug repurposing offers a promising way for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehe"},"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":"2005.10831","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2020-05-21T16:02:29Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"423b92ef02a3f3a18191f2eaf731f12dca725e53b47cce38dacb7d6817d83812","abstract_canon_sha256":"cc454df05870225cf251f19ffbe679e7ac0b37b8d80881cf885716f4b0415c07"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:05:03.085795Z","signature_b64":"rsj8jUBi9WUIQdslR9wervD2MoSfLYcY41BQw+yBMJNsCxohkOu5hE9fMJl1LqTzxRdOpXHN9iztX9Vx950vAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa62e5d64a1db4d1a3a6b5abc650a61ce664926b2d5a261df1721386dbc94e60","last_reissued_at":"2026-07-05T01:05:03.085298Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:05:03.085298Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-bio.QM","authors_text":"Feixiong Cheng, George Karypis, Tengfei Ma, Xiang Song, Xiangxiang Zeng, Xiaoqin Pan, Yadi Zhou, Yuan Hou, Zheng Zhang","submitted_at":"2020-05-21T16:02:29Z","abstract_excerpt":"There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there are currently no proven effective medications against COVID-19. Drug repurposing offers a promising way for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.10831","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/2005.10831/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":"2005.10831","created_at":"2026-07-05T01:05:03.085373+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.10831v1","created_at":"2026-07-05T01:05:03.085373+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.10831","created_at":"2026-07-05T01:05:03.085373+00:00"},{"alias_kind":"pith_short_12","alias_value":"VJROLVSKDW2N","created_at":"2026-07-05T01:05:03.085373+00:00"},{"alias_kind":"pith_short_16","alias_value":"VJROLVSKDW2NDI5G","created_at":"2026-07-05T01:05:03.085373+00:00"},{"alias_kind":"pith_short_8","alias_value":"VJROLVSK","created_at":"2026-07-05T01:05:03.085373+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/VJROLVSKDW2NDI5GWWV4MUFGDT","json":"https://pith.science/pith/VJROLVSKDW2NDI5GWWV4MUFGDT.json","graph_json":"https://pith.science/api/pith-number/VJROLVSKDW2NDI5GWWV4MUFGDT/graph.json","events_json":"https://pith.science/api/pith-number/VJROLVSKDW2NDI5GWWV4MUFGDT/events.json","paper":"https://pith.science/paper/VJROLVSK"},"agent_actions":{"view_html":"https://pith.science/pith/VJROLVSKDW2NDI5GWWV4MUFGDT","download_json":"https://pith.science/pith/VJROLVSKDW2NDI5GWWV4MUFGDT.json","view_paper":"https://pith.science/paper/VJROLVSK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.10831&json=true","fetch_graph":"https://pith.science/api/pith-number/VJROLVSKDW2NDI5GWWV4MUFGDT/graph.json","fetch_events":"https://pith.science/api/pith-number/VJROLVSKDW2NDI5GWWV4MUFGDT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VJROLVSKDW2NDI5GWWV4MUFGDT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VJROLVSKDW2NDI5GWWV4MUFGDT/action/storage_attestation","attest_author":"https://pith.science/pith/VJROLVSKDW2NDI5GWWV4MUFGDT/action/author_attestation","sign_citation":"https://pith.science/pith/VJROLVSKDW2NDI5GWWV4MUFGDT/action/citation_signature","submit_replication":"https://pith.science/pith/VJROLVSKDW2NDI5GWWV4MUFGDT/action/replication_record"}},"created_at":"2026-07-05T01:05:03.085373+00:00","updated_at":"2026-07-05T01:05:03.085373+00:00"}