{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BYEWDEGONR2RW2VVYN7CK3FRED","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":"eda0d41613fd926258fe52270cc6b326bd46bfb0d30305e11af8375e07820440","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T09:23:09Z","title_canon_sha256":"b6fb9ede302a81248e6cbf4d625aeb11c060dd978c1607cc8122fd3e020dfe6a"},"schema_version":"1.0","source":{"id":"2505.19740","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19740","created_at":"2026-07-05T11:09:35Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19740v1","created_at":"2026-07-05T11:09:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19740","created_at":"2026-07-05T11:09:35Z"},{"alias_kind":"pith_short_12","alias_value":"BYEWDEGONR2R","created_at":"2026-07-05T11:09:35Z"},{"alias_kind":"pith_short_16","alias_value":"BYEWDEGONR2RW2VV","created_at":"2026-07-05T11:09:35Z"},{"alias_kind":"pith_short_8","alias_value":"BYEWDEGO","created_at":"2026-07-05T11:09:35Z"}],"graph_snapshots":[{"event_id":"sha256:8134f5cc994b24be0c2286268469137dc93c73cff9d061592ac1c44d155d80c1","target":"graph","created_at":"2026-07-05T11:09:35Z","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/2505.19740/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this study, we propose a machine learning-based method for noise reduction and disease-causing gene feature extraction in gene sequencing DeepSeqDenoise algorithm combines CNN and RNN to effectively remove the sequencing noise, and improves the signal-to-noise ratio by 9.4 dB. We screened 17 key features by feature engineering, and constructed an integrated learning model to predict disease-causing genes with 94.3% accuracy. We successfully identified 57 new candidate disease-causing genes in a cardiovascular disease cohort validation, and detected 3 missed variants in clinical applications","authors_text":"Weichen Si, Yihao Ou, Zhen Tian","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T09:23:09Z","title":"Machine Learning Algorithm for Noise Reduction and Disease-Causing Gene Feature Extraction in Gene Sequencing Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19740","kind":"arxiv","version":1},"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":"sha256:37468d84a30f530442699353fd0f39505478c81046b81d21d4ef76253b6e660a","target":"record","created_at":"2026-07-05T11:09:35Z","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":"eda0d41613fd926258fe52270cc6b326bd46bfb0d30305e11af8375e07820440","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T09:23:09Z","title_canon_sha256":"b6fb9ede302a81248e6cbf4d625aeb11c060dd978c1607cc8122fd3e020dfe6a"},"schema_version":"1.0","source":{"id":"2505.19740","kind":"arxiv","version":1}},"canonical_sha256":"0e096190ce6c751b6ab5c37e256cb120f6cf0066be9730fc0031ab507197262f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0e096190ce6c751b6ab5c37e256cb120f6cf0066be9730fc0031ab507197262f","first_computed_at":"2026-07-05T11:09:35.527756Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:09:35.527756Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4lcuI3isAPIu3+phgErOMuhxDo/w6jfzz+ODKugzp4LrCOiWzGSuwD+Chfp1yt4BzRfo6vZyxCBnpAI9Ot/DCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:09:35.528207Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.19740","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:37468d84a30f530442699353fd0f39505478c81046b81d21d4ef76253b6e660a","sha256:8134f5cc994b24be0c2286268469137dc93c73cff9d061592ac1c44d155d80c1"],"state_sha256":"6affc3bbc51de144f16174d899984d7645838490ebab2cd6ea8bd5ca889fb0ef"}