{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:VFQ4NGDQJH7EYWUVE46EBBFGCH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"88048a15821617a2205c48a3b382884ba401161a2ab8893729a5018821603881","cross_cats_sorted":["cs.AI","cs.LG","q-bio.BM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2022-09-30T16:47:45Z","title_canon_sha256":"d47d699693ae20ae719ac1f7cdc7d289c8c0d64ad1b218a119149b5b3367bfad"},"schema_version":"1.0","source":{"id":"2210.00006","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.00006","created_at":"2026-07-05T05:39:53Z"},{"alias_kind":"arxiv_version","alias_value":"2210.00006v3","created_at":"2026-07-05T05:39:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.00006","created_at":"2026-07-05T05:39:53Z"},{"alias_kind":"pith_short_12","alias_value":"VFQ4NGDQJH7E","created_at":"2026-07-05T05:39:53Z"},{"alias_kind":"pith_short_16","alias_value":"VFQ4NGDQJH7EYWUV","created_at":"2026-07-05T05:39:53Z"},{"alias_kind":"pith_short_8","alias_value":"VFQ4NGDQ","created_at":"2026-07-05T05:39:53Z"}],"graph_snapshots":[{"event_id":"sha256:732240a7afacbee7a148182c9776ea1c25875a3d3f4929d1740f355e5dbee3f5","target":"graph","created_at":"2026-07-05T05:39:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2210.00006/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. Along with knowledge about the imaged molecules, cryo-EM maps allow de novo atomic modelling, which is typically done through a laborious manual process. Taking inspiration from recent advances in machine learning applications to protein structure prediction, we propose a graph neural network (GNN) approach for automated model building of proteins in cryo-EM maps. The GNN acts on a graph with nodes assigned to individual amino acids and edges ","authors_text":"Dari Kimanius, Kiarash Jamali, Sjors H.W. Scheres","cross_cats":["cs.AI","cs.LG","q-bio.BM"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2022-09-30T16:47:45Z","title":"A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.00006","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:34489fd7a964a3361ee165f0e5e1655d4e50f9ac970836edd2cdf3803546bcce","target":"record","created_at":"2026-07-05T05:39:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"88048a15821617a2205c48a3b382884ba401161a2ab8893729a5018821603881","cross_cats_sorted":["cs.AI","cs.LG","q-bio.BM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2022-09-30T16:47:45Z","title_canon_sha256":"d47d699693ae20ae719ac1f7cdc7d289c8c0d64ad1b218a119149b5b3367bfad"},"schema_version":"1.0","source":{"id":"2210.00006","kind":"arxiv","version":3}},"canonical_sha256":"a961c6987049fe4c5a95273c4084a611f2f7920ec2b7458fc3ac4d4092bb5b58","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a961c6987049fe4c5a95273c4084a611f2f7920ec2b7458fc3ac4d4092bb5b58","first_computed_at":"2026-07-05T05:39:53.501284Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:39:53.501284Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xmzsJJjQUV8Qbi3OORjFCglW6N5MdMj/+8fZlK1pffgEkFeBUQAforiq4BLtM0yBWCbMbwvsNhaM/5lpDRlDBw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:39:53.501673Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.00006","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:34489fd7a964a3361ee165f0e5e1655d4e50f9ac970836edd2cdf3803546bcce","sha256:732240a7afacbee7a148182c9776ea1c25875a3d3f4929d1740f355e5dbee3f5"],"state_sha256":"43acb556210326999e5493e591293900ff3e6baf137ffd17d4d6fa3fca1e15e9"}