{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:HSKSZIR6VHKRAHUWEBRM7UIMCR","short_pith_number":"pith:HSKSZIR6","canonical_record":{"source":{"id":"2102.02669","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.GN","submitted_at":"2021-02-03T07:34:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2adc7b0637b36f04d1e5d1f2ada99e287c0a1e593a6c8a24d962c7a3afbb540b","abstract_canon_sha256":"86240b95ea34c000041d7a6484a12b378aa8278170ee5807805dd911fe6e573e"},"schema_version":"1.0"},"canonical_sha256":"3c952ca23ea9d5101e962062cfd10c147a96d37c375061340313f35ff2f5a5c4","source":{"kind":"arxiv","id":"2102.02669","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.02669","created_at":"2026-07-05T02:50:43Z"},{"alias_kind":"arxiv_version","alias_value":"2102.02669v2","created_at":"2026-07-05T02:50:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.02669","created_at":"2026-07-05T02:50:43Z"},{"alias_kind":"pith_short_12","alias_value":"HSKSZIR6VHKR","created_at":"2026-07-05T02:50:43Z"},{"alias_kind":"pith_short_16","alias_value":"HSKSZIR6VHKRAHUW","created_at":"2026-07-05T02:50:43Z"},{"alias_kind":"pith_short_8","alias_value":"HSKSZIR6","created_at":"2026-07-05T02:50:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:HSKSZIR6VHKRAHUWEBRM7UIMCR","target":"record","payload":{"canonical_record":{"source":{"id":"2102.02669","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.GN","submitted_at":"2021-02-03T07:34:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2adc7b0637b36f04d1e5d1f2ada99e287c0a1e593a6c8a24d962c7a3afbb540b","abstract_canon_sha256":"86240b95ea34c000041d7a6484a12b378aa8278170ee5807805dd911fe6e573e"},"schema_version":"1.0"},"canonical_sha256":"3c952ca23ea9d5101e962062cfd10c147a96d37c375061340313f35ff2f5a5c4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:50:43.450973Z","signature_b64":"KH1F0L5/Tw/aFNBTuMi6hF1sIy82+Uli7XQJ8fsraQYX7BJgbHBWNXoOlTK4sNTAlZ3TRic5gDEBRrezuMhOBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c952ca23ea9d5101e962062cfd10c147a96d37c375061340313f35ff2f5a5c4","last_reissued_at":"2026-07-05T02:50:43.450594Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:50:43.450594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2102.02669","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:50:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9nIv7iJSirML5FM1aiXtEq+Uqdcdn7MvQEQ/mn3sG4dSYVmTLuQKZfgiFRvQIFP31RErIcpbOt1lpTZeix0SCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T13:52:33.617300Z"},"content_sha256":"a61cecf38962ffc165809a1e9dc014c79c91af350b12af1866500c20ecd00f5a","schema_version":"1.0","event_id":"sha256:a61cecf38962ffc165809a1e9dc014c79c91af350b12af1866500c20ecd00f5a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:HSKSZIR6VHKRAHUWEBRM7UIMCR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"OmiEmbed: a unified multi-task deep learning framework for multi-omics data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.GN","authors_text":"Kai Sun, Xiaoyu Zhang, Yike Guo, Yuting Xing","submitted_at":"2021-02-03T07:34:29Z","abstract_excerpt":"High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data due to the large number of molecular features and small number of available samples, which is also called 'the curse of dimensionality' in machine learning. To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and do"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.02669","kind":"arxiv","version":2},"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/2102.02669/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:50:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"574JT2BqAr+4dFivgGiDsBlu31M+TipKVZQ6q/K13LW8+8QHx2xnZoYN/8FCtMADKVtYJmfh1RZyRnCFSlONAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T13:52:33.617791Z"},"content_sha256":"731c7c7cb4ca4eb18da565bc8164956f61fd3b717f10dcf4db2afa3cbc0a9828","schema_version":"1.0","event_id":"sha256:731c7c7cb4ca4eb18da565bc8164956f61fd3b717f10dcf4db2afa3cbc0a9828"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HSKSZIR6VHKRAHUWEBRM7UIMCR/bundle.json","state_url":"https://pith.science/pith/HSKSZIR6VHKRAHUWEBRM7UIMCR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HSKSZIR6VHKRAHUWEBRM7UIMCR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-23T13:52:33Z","links":{"resolver":"https://pith.science/pith/HSKSZIR6VHKRAHUWEBRM7UIMCR","bundle":"https://pith.science/pith/HSKSZIR6VHKRAHUWEBRM7UIMCR/bundle.json","state":"https://pith.science/pith/HSKSZIR6VHKRAHUWEBRM7UIMCR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HSKSZIR6VHKRAHUWEBRM7UIMCR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:HSKSZIR6VHKRAHUWEBRM7UIMCR","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":"86240b95ea34c000041d7a6484a12b378aa8278170ee5807805dd911fe6e573e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.GN","submitted_at":"2021-02-03T07:34:29Z","title_canon_sha256":"2adc7b0637b36f04d1e5d1f2ada99e287c0a1e593a6c8a24d962c7a3afbb540b"},"schema_version":"1.0","source":{"id":"2102.02669","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.02669","created_at":"2026-07-05T02:50:43Z"},{"alias_kind":"arxiv_version","alias_value":"2102.02669v2","created_at":"2026-07-05T02:50:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.02669","created_at":"2026-07-05T02:50:43Z"},{"alias_kind":"pith_short_12","alias_value":"HSKSZIR6VHKR","created_at":"2026-07-05T02:50:43Z"},{"alias_kind":"pith_short_16","alias_value":"HSKSZIR6VHKRAHUW","created_at":"2026-07-05T02:50:43Z"},{"alias_kind":"pith_short_8","alias_value":"HSKSZIR6","created_at":"2026-07-05T02:50:43Z"}],"graph_snapshots":[{"event_id":"sha256:731c7c7cb4ca4eb18da565bc8164956f61fd3b717f10dcf4db2afa3cbc0a9828","target":"graph","created_at":"2026-07-05T02:50:43Z","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/2102.02669/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data due to the large number of molecular features and small number of available samples, which is also called 'the curse of dimensionality' in machine learning. To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and do","authors_text":"Kai Sun, Xiaoyu Zhang, Yike Guo, Yuting Xing","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.GN","submitted_at":"2021-02-03T07:34:29Z","title":"OmiEmbed: a unified multi-task deep learning framework for multi-omics data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.02669","kind":"arxiv","version":2},"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:a61cecf38962ffc165809a1e9dc014c79c91af350b12af1866500c20ecd00f5a","target":"record","created_at":"2026-07-05T02:50:43Z","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":"86240b95ea34c000041d7a6484a12b378aa8278170ee5807805dd911fe6e573e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.GN","submitted_at":"2021-02-03T07:34:29Z","title_canon_sha256":"2adc7b0637b36f04d1e5d1f2ada99e287c0a1e593a6c8a24d962c7a3afbb540b"},"schema_version":"1.0","source":{"id":"2102.02669","kind":"arxiv","version":2}},"canonical_sha256":"3c952ca23ea9d5101e962062cfd10c147a96d37c375061340313f35ff2f5a5c4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3c952ca23ea9d5101e962062cfd10c147a96d37c375061340313f35ff2f5a5c4","first_computed_at":"2026-07-05T02:50:43.450594Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:50:43.450594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KH1F0L5/Tw/aFNBTuMi6hF1sIy82+Uli7XQJ8fsraQYX7BJgbHBWNXoOlTK4sNTAlZ3TRic5gDEBRrezuMhOBg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:50:43.450973Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.02669","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a61cecf38962ffc165809a1e9dc014c79c91af350b12af1866500c20ecd00f5a","sha256:731c7c7cb4ca4eb18da565bc8164956f61fd3b717f10dcf4db2afa3cbc0a9828"],"state_sha256":"358b694ee3e446c821ba5d45076e13a3e72dba2c5116ced0ca17bed6aa7d20de"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ys0SsKC/AHTSAS222JRXdYjTfRuaQ6Ow6EKifT/ydBZJp7CC4e0Ib/BbCDbMlGwAYIcq08i/OzHe4Jd1May9Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T13:52:33.621425Z","bundle_sha256":"cebf04ca7fce162abfb24c73620b69eeb3fd1bad8b4959930506f14bec9b92e2"}}