{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:IR3KB6TNSY2JZQSXPVESTLXNGJ","short_pith_number":"pith:IR3KB6TN","schema_version":"1.0","canonical_sha256":"4476a0fa6d96349cc2577d4929aeed324fd4e892da2ca1d483fe6e01b619c1f6","source":{"kind":"arxiv","id":"1401.2832","version":1},"attestation_state":"computed","paper":{"title":"Principal trend analysis for time-course data with applications in genomic medicine","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Ronald Davis, Yuping Zhang","submitted_at":"2014-01-13T13:26:55Z","abstract_excerpt":"Time-course high-throughput gene expression data are emerging in genomic and translational medicine. Extracting interesting time-course patterns from a patient cohort can provide biological insights for further clinical research and patient treatment. We propose principal trend analysis (PTA) to extract principal trends of time-course gene expression data from a group of patients, and identify genes that make dominant contributions to the principal trends. Through simulations, we demonstrate the utility of PTA for dimension reduction, time-course signal recovery and feature selection with high"},"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":"1401.2832","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-01-13T13:26:55Z","cross_cats_sorted":[],"title_canon_sha256":"452cb0b36974dc4f4e22561a26cbecbd0031115c1ac5b58539fd94122c1cb77e","abstract_canon_sha256":"0615f69af5825ce982a10a2613343986649418e8bebd1c07ae3a503bc4722edd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:02:31.849033Z","signature_b64":"RPGh36Mh2ZSZfF3ErZNRujhx/S5AvQtsKeyHsSmew+MmSq5f0aEl1SDvtXeAgLvnkIjaQMn+KarS3XLLkm8UDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4476a0fa6d96349cc2577d4929aeed324fd4e892da2ca1d483fe6e01b619c1f6","last_reissued_at":"2026-05-18T03:02:31.848530Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:02:31.848530Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Principal trend analysis for time-course data with applications in genomic medicine","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Ronald Davis, Yuping Zhang","submitted_at":"2014-01-13T13:26:55Z","abstract_excerpt":"Time-course high-throughput gene expression data are emerging in genomic and translational medicine. Extracting interesting time-course patterns from a patient cohort can provide biological insights for further clinical research and patient treatment. We propose principal trend analysis (PTA) to extract principal trends of time-course gene expression data from a group of patients, and identify genes that make dominant contributions to the principal trends. Through simulations, we demonstrate the utility of PTA for dimension reduction, time-course signal recovery and feature selection with high"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1401.2832","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":""},"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":"1401.2832","created_at":"2026-05-18T03:02:31.848620+00:00"},{"alias_kind":"arxiv_version","alias_value":"1401.2832v1","created_at":"2026-05-18T03:02:31.848620+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1401.2832","created_at":"2026-05-18T03:02:31.848620+00:00"},{"alias_kind":"pith_short_12","alias_value":"IR3KB6TNSY2J","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_16","alias_value":"IR3KB6TNSY2JZQSX","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_8","alias_value":"IR3KB6TN","created_at":"2026-05-18T12:28:33.132498+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IR3KB6TNSY2JZQSXPVESTLXNGJ","json":"https://pith.science/pith/IR3KB6TNSY2JZQSXPVESTLXNGJ.json","graph_json":"https://pith.science/api/pith-number/IR3KB6TNSY2JZQSXPVESTLXNGJ/graph.json","events_json":"https://pith.science/api/pith-number/IR3KB6TNSY2JZQSXPVESTLXNGJ/events.json","paper":"https://pith.science/paper/IR3KB6TN"},"agent_actions":{"view_html":"https://pith.science/pith/IR3KB6TNSY2JZQSXPVESTLXNGJ","download_json":"https://pith.science/pith/IR3KB6TNSY2JZQSXPVESTLXNGJ.json","view_paper":"https://pith.science/paper/IR3KB6TN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1401.2832&json=true","fetch_graph":"https://pith.science/api/pith-number/IR3KB6TNSY2JZQSXPVESTLXNGJ/graph.json","fetch_events":"https://pith.science/api/pith-number/IR3KB6TNSY2JZQSXPVESTLXNGJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IR3KB6TNSY2JZQSXPVESTLXNGJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IR3KB6TNSY2JZQSXPVESTLXNGJ/action/storage_attestation","attest_author":"https://pith.science/pith/IR3KB6TNSY2JZQSXPVESTLXNGJ/action/author_attestation","sign_citation":"https://pith.science/pith/IR3KB6TNSY2JZQSXPVESTLXNGJ/action/citation_signature","submit_replication":"https://pith.science/pith/IR3KB6TNSY2JZQSXPVESTLXNGJ/action/replication_record"}},"created_at":"2026-05-18T03:02:31.848620+00:00","updated_at":"2026-05-18T03:02:31.848620+00:00"}