{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZMVMRLNGWQJRUKJJ45BF3MY4XZ","short_pith_number":"pith:ZMVMRLNG","schema_version":"1.0","canonical_sha256":"cb2ac8ada6b4131a2929e7425db31cbe7be946ab6cbd13643cbda27c5519d38d","source":{"kind":"arxiv","id":"2501.01477","version":1},"attestation_state":"computed","paper":{"title":"A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-bio.BM","authors_text":"Weihang Dai","submitted_at":"2025-01-02T05:21:34Z","abstract_excerpt":"Proteins are sequences of amino acids that serve as the basic building blocks of living organisms. Despite rapidly growing databases documenting structural and functional information for various protein sequences, our understanding of proteins remains limited because of the large possible sequence space and the complex inter- and intra-molecular forces. Deep learning, which is characterized by its ability to learn relevant features directly from large datasets, has demonstrated remarkable performance in fields such as computer vision and natural language processing. It has also been increasing"},"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":"2501.01477","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.BM","submitted_at":"2025-01-02T05:21:34Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2d6b987ac566df867ec54771ec360750c3439218d7948957ecd6ddaa5d65e193","abstract_canon_sha256":"e8f29e5b68c8459cbd80e879ad10d06a8408fdd4c6bc326b04b3bf196cf79c46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:25.355394Z","signature_b64":"F9ugoCvaYMfn1pqlPSGmpd/HWuEsZKZ0bQJEt6gPPDY7eWs+/5g3fj4WhP0qaNkiQdGUDNmEZYvAPiKBgGgcDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb2ac8ada6b4131a2929e7425db31cbe7be946ab6cbd13643cbda27c5519d38d","last_reissued_at":"2026-07-05T09:56:25.354887Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:25.354887Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-bio.BM","authors_text":"Weihang Dai","submitted_at":"2025-01-02T05:21:34Z","abstract_excerpt":"Proteins are sequences of amino acids that serve as the basic building blocks of living organisms. Despite rapidly growing databases documenting structural and functional information for various protein sequences, our understanding of proteins remains limited because of the large possible sequence space and the complex inter- and intra-molecular forces. Deep learning, which is characterized by its ability to learn relevant features directly from large datasets, has demonstrated remarkable performance in fields such as computer vision and natural language processing. It has also been increasing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01477","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/2501.01477/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":"2501.01477","created_at":"2026-07-05T09:56:25.354949+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.01477v1","created_at":"2026-07-05T09:56:25.354949+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01477","created_at":"2026-07-05T09:56:25.354949+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZMVMRLNGWQJR","created_at":"2026-07-05T09:56:25.354949+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZMVMRLNGWQJRUKJJ","created_at":"2026-07-05T09:56:25.354949+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZMVMRLNG","created_at":"2026-07-05T09:56:25.354949+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.29228","citing_title":"Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZMVMRLNGWQJRUKJJ45BF3MY4XZ","json":"https://pith.science/pith/ZMVMRLNGWQJRUKJJ45BF3MY4XZ.json","graph_json":"https://pith.science/api/pith-number/ZMVMRLNGWQJRUKJJ45BF3MY4XZ/graph.json","events_json":"https://pith.science/api/pith-number/ZMVMRLNGWQJRUKJJ45BF3MY4XZ/events.json","paper":"https://pith.science/paper/ZMVMRLNG"},"agent_actions":{"view_html":"https://pith.science/pith/ZMVMRLNGWQJRUKJJ45BF3MY4XZ","download_json":"https://pith.science/pith/ZMVMRLNGWQJRUKJJ45BF3MY4XZ.json","view_paper":"https://pith.science/paper/ZMVMRLNG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.01477&json=true","fetch_graph":"https://pith.science/api/pith-number/ZMVMRLNGWQJRUKJJ45BF3MY4XZ/graph.json","fetch_events":"https://pith.science/api/pith-number/ZMVMRLNGWQJRUKJJ45BF3MY4XZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZMVMRLNGWQJRUKJJ45BF3MY4XZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZMVMRLNGWQJRUKJJ45BF3MY4XZ/action/storage_attestation","attest_author":"https://pith.science/pith/ZMVMRLNGWQJRUKJJ45BF3MY4XZ/action/author_attestation","sign_citation":"https://pith.science/pith/ZMVMRLNGWQJRUKJJ45BF3MY4XZ/action/citation_signature","submit_replication":"https://pith.science/pith/ZMVMRLNGWQJRUKJJ45BF3MY4XZ/action/replication_record"}},"created_at":"2026-07-05T09:56:25.354949+00:00","updated_at":"2026-07-05T09:56:25.354949+00:00"}