{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:BULYF75HIQXFA7MCOOLRJAEWQL","short_pith_number":"pith:BULYF75H","schema_version":"1.0","canonical_sha256":"0d1782ffa7442e507d82739714809682e5896e8c1c7e32e98c28499e0d2b6a7d","source":{"kind":"arxiv","id":"2005.04010","version":1},"attestation_state":"computed","paper":{"title":"Flexible co-data learning for high-dimensional prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Lodewyk F.A. Wessels, Mark A. van de Wiel, Mirrelijn M. van Nee","submitted_at":"2020-05-08T13:04:31Z","abstract_excerpt":"Clinical research often focuses on complex traits in which many variables play a role in mechanisms driving, or curing, diseases. Clinical prediction is hard when data is high-dimensional, but additional information, like domain knowledge and previously published studies, may be helpful to improve predictions. Such complementary data, or co-data, provide information on the covariates, such as genomic location or p-values from external studies. Our method enables exploiting multiple and various co-data sources to improve predictions. We use discrete or continuous co-data to define possibly over"},"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":"2005.04010","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-05-08T13:04:31Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"02936c40545546e4f8fb0df63613416279072a4b96d7282d3c15210c8ac85701","abstract_canon_sha256":"d107a0460d35fabf7a146b901d08844ec6f71cf6366fb395f2684055936c54ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:04:32.267766Z","signature_b64":"BKAik8gLUAHyuovxT1X590IqgaxMYWnP9j+wFHOALzcWFC5t6ifaBDrwO0fna+a0kD5RcfHxggi1dnkZHvCUBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d1782ffa7442e507d82739714809682e5896e8c1c7e32e98c28499e0d2b6a7d","last_reissued_at":"2026-07-05T01:04:32.267353Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:04:32.267353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Flexible co-data learning for high-dimensional prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Lodewyk F.A. Wessels, Mark A. van de Wiel, Mirrelijn M. van Nee","submitted_at":"2020-05-08T13:04:31Z","abstract_excerpt":"Clinical research often focuses on complex traits in which many variables play a role in mechanisms driving, or curing, diseases. Clinical prediction is hard when data is high-dimensional, but additional information, like domain knowledge and previously published studies, may be helpful to improve predictions. Such complementary data, or co-data, provide information on the covariates, such as genomic location or p-values from external studies. Our method enables exploiting multiple and various co-data sources to improve predictions. We use discrete or continuous co-data to define possibly over"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.04010","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/2005.04010/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":"2005.04010","created_at":"2026-07-05T01:04:32.267410+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.04010v1","created_at":"2026-07-05T01:04:32.267410+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.04010","created_at":"2026-07-05T01:04:32.267410+00:00"},{"alias_kind":"pith_short_12","alias_value":"BULYF75HIQXF","created_at":"2026-07-05T01:04:32.267410+00:00"},{"alias_kind":"pith_short_16","alias_value":"BULYF75HIQXFA7MC","created_at":"2026-07-05T01:04:32.267410+00:00"},{"alias_kind":"pith_short_8","alias_value":"BULYF75H","created_at":"2026-07-05T01:04:32.267410+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/BULYF75HIQXFA7MCOOLRJAEWQL","json":"https://pith.science/pith/BULYF75HIQXFA7MCOOLRJAEWQL.json","graph_json":"https://pith.science/api/pith-number/BULYF75HIQXFA7MCOOLRJAEWQL/graph.json","events_json":"https://pith.science/api/pith-number/BULYF75HIQXFA7MCOOLRJAEWQL/events.json","paper":"https://pith.science/paper/BULYF75H"},"agent_actions":{"view_html":"https://pith.science/pith/BULYF75HIQXFA7MCOOLRJAEWQL","download_json":"https://pith.science/pith/BULYF75HIQXFA7MCOOLRJAEWQL.json","view_paper":"https://pith.science/paper/BULYF75H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.04010&json=true","fetch_graph":"https://pith.science/api/pith-number/BULYF75HIQXFA7MCOOLRJAEWQL/graph.json","fetch_events":"https://pith.science/api/pith-number/BULYF75HIQXFA7MCOOLRJAEWQL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BULYF75HIQXFA7MCOOLRJAEWQL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BULYF75HIQXFA7MCOOLRJAEWQL/action/storage_attestation","attest_author":"https://pith.science/pith/BULYF75HIQXFA7MCOOLRJAEWQL/action/author_attestation","sign_citation":"https://pith.science/pith/BULYF75HIQXFA7MCOOLRJAEWQL/action/citation_signature","submit_replication":"https://pith.science/pith/BULYF75HIQXFA7MCOOLRJAEWQL/action/replication_record"}},"created_at":"2026-07-05T01:04:32.267410+00:00","updated_at":"2026-07-05T01:04:32.267410+00:00"}