{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:I4EGSE3AVA6NQ4USR63KSXPDXU","short_pith_number":"pith:I4EGSE3A","schema_version":"1.0","canonical_sha256":"4708691360a83cd872928fb6a95de3bd38df5398238317fa9ed05d539be46a48","source":{"kind":"arxiv","id":"1709.03645","version":1},"attestation_state":"computed","paper":{"title":"Identifying Genetic Risk Factors via Sparse Group Lasso with Group Graph Structure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.GN"],"primary_cat":"stat.ML","authors_text":"Jieping Ye, Paul Thompson, Sihai Zhao, Tao Yang","submitted_at":"2017-09-12T01:34:50Z","abstract_excerpt":"Genome-wide association studies (GWA studies or GWAS) investigate the relationships between genetic variants such as single-nucleotide polymorphisms (SNPs) and individual traits. Recently, incorporating biological priors together with machine learning methods in GWA studies has attracted increasing attention. However, in real-world, nucleotide-level bio-priors have not been well-studied to date. Alternatively, studies at gene-level, for example, protein--protein interactions and pathways, are more rigorous and legitimate, and it is potentially beneficial to utilize such gene-level priors in GW"},"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":"1709.03645","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-12T01:34:50Z","cross_cats_sorted":["cs.LG","q-bio.GN"],"title_canon_sha256":"5d8d1b3e3c2d35c6a8a623e03a70596115127cef3b85558d8603357353e310ac","abstract_canon_sha256":"0cc59f101ad6a170ab9ef4a38a2e03da14915b892a2b8bc289b7a2db784055a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:29.945663Z","signature_b64":"YqMXUMXgLpZlGBOSdd+lP9WGEVqUwAjnXJpEST7CGyPWolCfmkwJBBgn1gwQhmLmMOHXGJI9b55h8djyaOfXCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4708691360a83cd872928fb6a95de3bd38df5398238317fa9ed05d539be46a48","last_reissued_at":"2026-05-18T00:35:29.945014Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:29.945014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Identifying Genetic Risk Factors via Sparse Group Lasso with Group Graph Structure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.GN"],"primary_cat":"stat.ML","authors_text":"Jieping Ye, Paul Thompson, Sihai Zhao, Tao Yang","submitted_at":"2017-09-12T01:34:50Z","abstract_excerpt":"Genome-wide association studies (GWA studies or GWAS) investigate the relationships between genetic variants such as single-nucleotide polymorphisms (SNPs) and individual traits. Recently, incorporating biological priors together with machine learning methods in GWA studies has attracted increasing attention. However, in real-world, nucleotide-level bio-priors have not been well-studied to date. Alternatively, studies at gene-level, for example, protein--protein interactions and pathways, are more rigorous and legitimate, and it is potentially beneficial to utilize such gene-level priors in GW"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.03645","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":"1709.03645","created_at":"2026-05-18T00:35:29.945127+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.03645v1","created_at":"2026-05-18T00:35:29.945127+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.03645","created_at":"2026-05-18T00:35:29.945127+00:00"},{"alias_kind":"pith_short_12","alias_value":"I4EGSE3AVA6N","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_16","alias_value":"I4EGSE3AVA6NQ4US","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_8","alias_value":"I4EGSE3A","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/I4EGSE3AVA6NQ4USR63KSXPDXU","json":"https://pith.science/pith/I4EGSE3AVA6NQ4USR63KSXPDXU.json","graph_json":"https://pith.science/api/pith-number/I4EGSE3AVA6NQ4USR63KSXPDXU/graph.json","events_json":"https://pith.science/api/pith-number/I4EGSE3AVA6NQ4USR63KSXPDXU/events.json","paper":"https://pith.science/paper/I4EGSE3A"},"agent_actions":{"view_html":"https://pith.science/pith/I4EGSE3AVA6NQ4USR63KSXPDXU","download_json":"https://pith.science/pith/I4EGSE3AVA6NQ4USR63KSXPDXU.json","view_paper":"https://pith.science/paper/I4EGSE3A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.03645&json=true","fetch_graph":"https://pith.science/api/pith-number/I4EGSE3AVA6NQ4USR63KSXPDXU/graph.json","fetch_events":"https://pith.science/api/pith-number/I4EGSE3AVA6NQ4USR63KSXPDXU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I4EGSE3AVA6NQ4USR63KSXPDXU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I4EGSE3AVA6NQ4USR63KSXPDXU/action/storage_attestation","attest_author":"https://pith.science/pith/I4EGSE3AVA6NQ4USR63KSXPDXU/action/author_attestation","sign_citation":"https://pith.science/pith/I4EGSE3AVA6NQ4USR63KSXPDXU/action/citation_signature","submit_replication":"https://pith.science/pith/I4EGSE3AVA6NQ4USR63KSXPDXU/action/replication_record"}},"created_at":"2026-05-18T00:35:29.945127+00:00","updated_at":"2026-05-18T00:35:29.945127+00:00"}