{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:B6PWGOOENLQQSTXAC7OFHLKYMF","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":"c597f774dd455f424402a87911478e54a60734f0ea8d75c6dd52c522fa9091e6","cross_cats_sorted":["cs.LG","q-bio.BM"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"q-bio.QM","submitted_at":"2024-06-20T09:34:31Z","title_canon_sha256":"c6d9fcc28e89df7f94ac06952c08a74e1c76a87624403acc559882afd164ff3d"},"schema_version":"1.0","source":{"id":"2406.14142","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.14142","created_at":"2026-07-05T09:22:40Z"},{"alias_kind":"arxiv_version","alias_value":"2406.14142v3","created_at":"2026-07-05T09:22:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.14142","created_at":"2026-07-05T09:22:40Z"},{"alias_kind":"pith_short_12","alias_value":"B6PWGOOENLQQ","created_at":"2026-07-05T09:22:40Z"},{"alias_kind":"pith_short_16","alias_value":"B6PWGOOENLQQSTXA","created_at":"2026-07-05T09:22:40Z"},{"alias_kind":"pith_short_8","alias_value":"B6PWGOOE","created_at":"2026-07-05T09:22:40Z"}],"graph_snapshots":[{"event_id":"sha256:1959afaff2c0056021a3c2666d64823b8c73b469feba4c27bfe3bc8e576409e0","target":"graph","created_at":"2026-07-05T09:22:40Z","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/2406.14142/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Protein representation learning aims to learn informative protein embeddings capable of addressing crucial biological questions, such as protein function prediction. Although sequence-based transformer models have shown promising results by leveraging the vast amount of protein sequence data in a self-supervised way, there is still a gap in exploiting the available 3D protein structures. In this work, we propose a pre-training scheme going beyond trivial masking methods leveraging 3D and hierarchical structures of proteins. We propose a novel self-supervised method to pretrain 3D graph neural ","authors_text":"George Dasoulas, Michail Chatzianastasis, Michalis Vazirgiannis, Yang Zhang","cross_cats":["cs.LG","q-bio.BM"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"q-bio.QM","submitted_at":"2024-06-20T09:34:31Z","title":"Geometric Self-Supervised Pretraining on 3D Protein Structures using Subgraphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.14142","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":"sha256:25e1d6613235daf8c880d8f4ad69ada1282709d41987ddd96305b51e14ce6d20","target":"record","created_at":"2026-07-05T09:22:40Z","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":"c597f774dd455f424402a87911478e54a60734f0ea8d75c6dd52c522fa9091e6","cross_cats_sorted":["cs.LG","q-bio.BM"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"q-bio.QM","submitted_at":"2024-06-20T09:34:31Z","title_canon_sha256":"c6d9fcc28e89df7f94ac06952c08a74e1c76a87624403acc559882afd164ff3d"},"schema_version":"1.0","source":{"id":"2406.14142","kind":"arxiv","version":3}},"canonical_sha256":"0f9f6339c46ae1094ee017dc53ad58617204041eb317cc08942cc70b3ad4581a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0f9f6339c46ae1094ee017dc53ad58617204041eb317cc08942cc70b3ad4581a","first_computed_at":"2026-07-05T09:22:40.748155Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:22:40.748155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MS49BZe0eO+dgEUGNYW0H4kOBDcWqO4gC+s4ObzxDvP6457cuaLWe5E98k4iUvk8Hf4kcSe13YkA4Xh1t5+/Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:22:40.748808Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.14142","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:25e1d6613235daf8c880d8f4ad69ada1282709d41987ddd96305b51e14ce6d20","sha256:1959afaff2c0056021a3c2666d64823b8c73b469feba4c27bfe3bc8e576409e0"],"state_sha256":"775039cf4e1bea8efe90dffd51d65ee6af405ee30505efd3fbbfebed69f4f9e9"}