{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ORA6DHRB3FIC7VTAEZXOEAKJGT","short_pith_number":"pith:ORA6DHRB","schema_version":"1.0","canonical_sha256":"7441e19e21d9502fd660266ee2014934e20cc39ae328a8da5cf7217e1d3415f1","source":{"kind":"arxiv","id":"2203.13132","version":1},"attestation_state":"computed","paper":{"title":"DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.BM"],"primary_cat":"q-bio.QM","authors_text":"Eric Stone, Khandaker Asif, Liyuan Pan, Shafin Rahman, Yan Yang, Zakir Hossain","submitted_at":"2022-03-23T08:01:06Z","abstract_excerpt":"De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data. Existing approaches for de novo analysis enumerate MS evidence for all amino acid classes during inference. It leads to over-trimming on receptive fields of MS data and restricts MS evidence associated with following undecoded amino acids. Our approach, DPST, circumvents these limitations with two key components: (1) A confidence value aggregation encoder to sketch spectrum representations according to amino-acid-based connectivity among MS; (2) A global-local fusion decoder to"},"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":"2203.13132","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2022-03-23T08:01:06Z","cross_cats_sorted":["cs.LG","q-bio.BM"],"title_canon_sha256":"9dfdcb002ec9ee6d7e09e1098ce2e0632601f5bc4270d34a6c5c2dca92b1a238","abstract_canon_sha256":"885434452f76964b84f1e19b616f5eefbfa8332d58e123c4c195631a271a4862"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:08:21.046788Z","signature_b64":"cPk9J60XAzl6WqZUpg6dglK3d+KMzdP6uEGS+XFw5qsr8hxLYNW/dOcG/Gt4Ip4aAiQ8LRDsq4Aikc5H6RYqDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7441e19e21d9502fd660266ee2014934e20cc39ae328a8da5cf7217e1d3415f1","last_reissued_at":"2026-07-05T04:08:21.046177Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:08:21.046177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.BM"],"primary_cat":"q-bio.QM","authors_text":"Eric Stone, Khandaker Asif, Liyuan Pan, Shafin Rahman, Yan Yang, Zakir Hossain","submitted_at":"2022-03-23T08:01:06Z","abstract_excerpt":"De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data. Existing approaches for de novo analysis enumerate MS evidence for all amino acid classes during inference. It leads to over-trimming on receptive fields of MS data and restricts MS evidence associated with following undecoded amino acids. Our approach, DPST, circumvents these limitations with two key components: (1) A confidence value aggregation encoder to sketch spectrum representations according to amino-acid-based connectivity among MS; (2) A global-local fusion decoder to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.13132","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/2203.13132/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":"2203.13132","created_at":"2026-07-05T04:08:21.046235+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.13132v1","created_at":"2026-07-05T04:08:21.046235+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.13132","created_at":"2026-07-05T04:08:21.046235+00:00"},{"alias_kind":"pith_short_12","alias_value":"ORA6DHRB3FIC","created_at":"2026-07-05T04:08:21.046235+00:00"},{"alias_kind":"pith_short_16","alias_value":"ORA6DHRB3FIC7VTA","created_at":"2026-07-05T04:08:21.046235+00:00"},{"alias_kind":"pith_short_8","alias_value":"ORA6DHRB","created_at":"2026-07-05T04:08:21.046235+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11868","citing_title":"MemNovo: Look Back at the Spectrum for Balanced De Novo Peptide Sequencing from Mass Spectrometry","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ORA6DHRB3FIC7VTAEZXOEAKJGT","json":"https://pith.science/pith/ORA6DHRB3FIC7VTAEZXOEAKJGT.json","graph_json":"https://pith.science/api/pith-number/ORA6DHRB3FIC7VTAEZXOEAKJGT/graph.json","events_json":"https://pith.science/api/pith-number/ORA6DHRB3FIC7VTAEZXOEAKJGT/events.json","paper":"https://pith.science/paper/ORA6DHRB"},"agent_actions":{"view_html":"https://pith.science/pith/ORA6DHRB3FIC7VTAEZXOEAKJGT","download_json":"https://pith.science/pith/ORA6DHRB3FIC7VTAEZXOEAKJGT.json","view_paper":"https://pith.science/paper/ORA6DHRB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.13132&json=true","fetch_graph":"https://pith.science/api/pith-number/ORA6DHRB3FIC7VTAEZXOEAKJGT/graph.json","fetch_events":"https://pith.science/api/pith-number/ORA6DHRB3FIC7VTAEZXOEAKJGT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ORA6DHRB3FIC7VTAEZXOEAKJGT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ORA6DHRB3FIC7VTAEZXOEAKJGT/action/storage_attestation","attest_author":"https://pith.science/pith/ORA6DHRB3FIC7VTAEZXOEAKJGT/action/author_attestation","sign_citation":"https://pith.science/pith/ORA6DHRB3FIC7VTAEZXOEAKJGT/action/citation_signature","submit_replication":"https://pith.science/pith/ORA6DHRB3FIC7VTAEZXOEAKJGT/action/replication_record"}},"created_at":"2026-07-05T04:08:21.046235+00:00","updated_at":"2026-07-05T04:08:21.046235+00:00"}