{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:RS6EH3F6HTI4MSUGNZJCYXZHWZ","short_pith_number":"pith:RS6EH3F6","schema_version":"1.0","canonical_sha256":"8cbc43ecbe3cd1c64a866e522c5f27b66ece8552220c51836909cf51ebe8e182","source":{"kind":"arxiv","id":"1906.11196","version":1},"attestation_state":"computed","paper":{"title":"Seq-SetNet: Exploring Sequence Sets for Inferring Structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-bio.BM","authors_text":"Dongbo Bu, Fusong Ju, Guozheng Wei, Jianwei Zhu, Qi Zhang, Shiwei Sun","submitted_at":"2019-06-06T12:41:00Z","abstract_excerpt":"Sequence set is a widely-used type of data source in a large variety of fields. A typical example is protein structure prediction, which takes an multiple sequence alignment (MSA) as input and aims to infer structural information from it. Almost all of the existing approaches exploit MSAs in an indirect fashion, i.e., they transform MSAs into position-specific scoring matrices (PSSM) that represent the distribution of amino acid types at each column. PSSM could capture column-wise characteristics of MSA, however, the column-wise characteristics embedded in each individual component sequence we"},"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":"1906.11196","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.BM","submitted_at":"2019-06-06T12:41:00Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"5a9c6f4294e252a7a5c7f478469a8a39f5da401ef82edff11b62ce654eeb7968","abstract_canon_sha256":"23beb1545c3d39e676d395e031cd39c953518723f9a5e81a02efecebb6fcd9a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:09.508166Z","signature_b64":"rKMVyPGTCfdMcjIXXfLivDBez2XDZzhYJRmRGF0+2xjq4CSFhF8o3kPCVWgZMpJ2+mdihwJ3/bnBn2diWQT/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8cbc43ecbe3cd1c64a866e522c5f27b66ece8552220c51836909cf51ebe8e182","last_reissued_at":"2026-05-17T23:42:09.507560Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:09.507560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Seq-SetNet: Exploring Sequence Sets for Inferring Structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-bio.BM","authors_text":"Dongbo Bu, Fusong Ju, Guozheng Wei, Jianwei Zhu, Qi Zhang, Shiwei Sun","submitted_at":"2019-06-06T12:41:00Z","abstract_excerpt":"Sequence set is a widely-used type of data source in a large variety of fields. A typical example is protein structure prediction, which takes an multiple sequence alignment (MSA) as input and aims to infer structural information from it. Almost all of the existing approaches exploit MSAs in an indirect fashion, i.e., they transform MSAs into position-specific scoring matrices (PSSM) that represent the distribution of amino acid types at each column. PSSM could capture column-wise characteristics of MSA, however, the column-wise characteristics embedded in each individual component sequence we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11196","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":"1906.11196","created_at":"2026-05-17T23:42:09.507662+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.11196v1","created_at":"2026-05-17T23:42:09.507662+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11196","created_at":"2026-05-17T23:42:09.507662+00:00"},{"alias_kind":"pith_short_12","alias_value":"RS6EH3F6HTI4","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"RS6EH3F6HTI4MSUG","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"RS6EH3F6","created_at":"2026-05-18T12:33:27.125529+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/RS6EH3F6HTI4MSUGNZJCYXZHWZ","json":"https://pith.science/pith/RS6EH3F6HTI4MSUGNZJCYXZHWZ.json","graph_json":"https://pith.science/api/pith-number/RS6EH3F6HTI4MSUGNZJCYXZHWZ/graph.json","events_json":"https://pith.science/api/pith-number/RS6EH3F6HTI4MSUGNZJCYXZHWZ/events.json","paper":"https://pith.science/paper/RS6EH3F6"},"agent_actions":{"view_html":"https://pith.science/pith/RS6EH3F6HTI4MSUGNZJCYXZHWZ","download_json":"https://pith.science/pith/RS6EH3F6HTI4MSUGNZJCYXZHWZ.json","view_paper":"https://pith.science/paper/RS6EH3F6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.11196&json=true","fetch_graph":"https://pith.science/api/pith-number/RS6EH3F6HTI4MSUGNZJCYXZHWZ/graph.json","fetch_events":"https://pith.science/api/pith-number/RS6EH3F6HTI4MSUGNZJCYXZHWZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RS6EH3F6HTI4MSUGNZJCYXZHWZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RS6EH3F6HTI4MSUGNZJCYXZHWZ/action/storage_attestation","attest_author":"https://pith.science/pith/RS6EH3F6HTI4MSUGNZJCYXZHWZ/action/author_attestation","sign_citation":"https://pith.science/pith/RS6EH3F6HTI4MSUGNZJCYXZHWZ/action/citation_signature","submit_replication":"https://pith.science/pith/RS6EH3F6HTI4MSUGNZJCYXZHWZ/action/replication_record"}},"created_at":"2026-05-17T23:42:09.507662+00:00","updated_at":"2026-05-17T23:42:09.507662+00:00"}