{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:J42ONUMUB5YE5JGAT6YWPL74CY","short_pith_number":"pith:J42ONUMU","schema_version":"1.0","canonical_sha256":"4f34e6d1940f704ea4c09fb167affc161cd7fc5e312b786d8e7c1ee265cec66f","source":{"kind":"arxiv","id":"1711.00150","version":1},"attestation_state":"computed","paper":{"title":"Erratum: Link prediction in drug-target interactions network using similarity indices","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anna Korhonen, Yiding Lu, Yufan Guo","submitted_at":"2017-11-01T00:21:48Z","abstract_excerpt":"Background: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome"},"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":"1711.00150","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2017-11-01T00:21:48Z","cross_cats_sorted":[],"title_canon_sha256":"565a2f670c3ef90b3f3f041bb85be498a9c65de24ec28a9429d0644bce3c33aa","abstract_canon_sha256":"d17039677e7e455652a13ecae57ea2661505d6c5f012ecb87ae368609e000250"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:35.044779Z","signature_b64":"1wpcrqQMtGr72v+w4wcQShXbN4S1mXXx/jm5rIikdYi+QTGA/dQVngJIegpOU+IxJaeNa/ucYREL4TBcuxq4Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f34e6d1940f704ea4c09fb167affc161cd7fc5e312b786d8e7c1ee265cec66f","last_reissued_at":"2026-05-18T00:31:35.044065Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:35.044065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Erratum: Link prediction in drug-target interactions network using similarity indices","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anna Korhonen, Yiding Lu, Yufan Guo","submitted_at":"2017-11-01T00:21:48Z","abstract_excerpt":"Background: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00150","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":""},"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":"1711.00150","created_at":"2026-05-18T00:31:35.044180+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.00150v1","created_at":"2026-05-18T00:31:35.044180+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00150","created_at":"2026-05-18T00:31:35.044180+00:00"},{"alias_kind":"pith_short_12","alias_value":"J42ONUMUB5YE","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_16","alias_value":"J42ONUMUB5YE5JGA","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_8","alias_value":"J42ONUMU","created_at":"2026-05-18T12:31:21.493067+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/J42ONUMUB5YE5JGAT6YWPL74CY","json":"https://pith.science/pith/J42ONUMUB5YE5JGAT6YWPL74CY.json","graph_json":"https://pith.science/api/pith-number/J42ONUMUB5YE5JGAT6YWPL74CY/graph.json","events_json":"https://pith.science/api/pith-number/J42ONUMUB5YE5JGAT6YWPL74CY/events.json","paper":"https://pith.science/paper/J42ONUMU"},"agent_actions":{"view_html":"https://pith.science/pith/J42ONUMUB5YE5JGAT6YWPL74CY","download_json":"https://pith.science/pith/J42ONUMUB5YE5JGAT6YWPL74CY.json","view_paper":"https://pith.science/paper/J42ONUMU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.00150&json=true","fetch_graph":"https://pith.science/api/pith-number/J42ONUMUB5YE5JGAT6YWPL74CY/graph.json","fetch_events":"https://pith.science/api/pith-number/J42ONUMUB5YE5JGAT6YWPL74CY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J42ONUMUB5YE5JGAT6YWPL74CY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J42ONUMUB5YE5JGAT6YWPL74CY/action/storage_attestation","attest_author":"https://pith.science/pith/J42ONUMUB5YE5JGAT6YWPL74CY/action/author_attestation","sign_citation":"https://pith.science/pith/J42ONUMUB5YE5JGAT6YWPL74CY/action/citation_signature","submit_replication":"https://pith.science/pith/J42ONUMUB5YE5JGAT6YWPL74CY/action/replication_record"}},"created_at":"2026-05-18T00:31:35.044180+00:00","updated_at":"2026-05-18T00:31:35.044180+00:00"}