{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:VZCIXJNIG6GNWENOSA6HL22DV6","short_pith_number":"pith:VZCIXJNI","schema_version":"1.0","canonical_sha256":"ae448ba5a8378cdb11ae903c75eb43afbadd4f0b7ee243b727faef6b136b2101","source":{"kind":"arxiv","id":"1301.4144","version":1},"attestation_state":"computed","paper":{"title":"Non-parametric Bayesian modelling of digital gene expression data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.GN","stat.AP","stat.ML"],"primary_cat":"q-bio.QM","authors_text":"Dimitrios V. Vavoulis, Julian Gough","submitted_at":"2013-01-17T16:08:00Z","abstract_excerpt":"Next-generation sequencing technologies provide a revolutionary tool for generating gene expression data. Starting with a fixed RNA sample, they construct a library of millions of differentially abundant short sequence tags or \"reads\", which constitute a fundamentally discrete measure of the level of gene expression. A common limitation in experiments using these technologies is the low number or even absence of biological replicates, which complicates the statistical analysis of digital gene expression data. Analysis of this type of data has often been based on modified tests originally devis"},"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":"1301.4144","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2013-01-17T16:08:00Z","cross_cats_sorted":["q-bio.GN","stat.AP","stat.ML"],"title_canon_sha256":"ab7f405b919f6db1c6ffa9ce2430e44a916898ed9199b8a3d0826c5e77a187f5","abstract_canon_sha256":"60d33a6218948818ad6e7daa69530403da05565e12265797924612f6b1b303ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:52:12.816213Z","signature_b64":"aXxMr03Zf1q29mHrb2GHcKy4fZJ07CRlA8tUcVV0vlbCS2gE65WGyL/b3WPnL6QAuLYD8CxEe1T1ARi55UN1Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae448ba5a8378cdb11ae903c75eb43afbadd4f0b7ee243b727faef6b136b2101","last_reissued_at":"2026-05-18T02:52:12.815589Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:52:12.815589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Non-parametric Bayesian modelling of digital gene expression data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.GN","stat.AP","stat.ML"],"primary_cat":"q-bio.QM","authors_text":"Dimitrios V. Vavoulis, Julian Gough","submitted_at":"2013-01-17T16:08:00Z","abstract_excerpt":"Next-generation sequencing technologies provide a revolutionary tool for generating gene expression data. Starting with a fixed RNA sample, they construct a library of millions of differentially abundant short sequence tags or \"reads\", which constitute a fundamentally discrete measure of the level of gene expression. A common limitation in experiments using these technologies is the low number or even absence of biological replicates, which complicates the statistical analysis of digital gene expression data. Analysis of this type of data has often been based on modified tests originally devis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.4144","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":"1301.4144","created_at":"2026-05-18T02:52:12.815685+00:00"},{"alias_kind":"arxiv_version","alias_value":"1301.4144v1","created_at":"2026-05-18T02:52:12.815685+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.4144","created_at":"2026-05-18T02:52:12.815685+00:00"},{"alias_kind":"pith_short_12","alias_value":"VZCIXJNIG6GN","created_at":"2026-05-18T12:28:04.890932+00:00"},{"alias_kind":"pith_short_16","alias_value":"VZCIXJNIG6GNWENO","created_at":"2026-05-18T12:28:04.890932+00:00"},{"alias_kind":"pith_short_8","alias_value":"VZCIXJNI","created_at":"2026-05-18T12:28:04.890932+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/VZCIXJNIG6GNWENOSA6HL22DV6","json":"https://pith.science/pith/VZCIXJNIG6GNWENOSA6HL22DV6.json","graph_json":"https://pith.science/api/pith-number/VZCIXJNIG6GNWENOSA6HL22DV6/graph.json","events_json":"https://pith.science/api/pith-number/VZCIXJNIG6GNWENOSA6HL22DV6/events.json","paper":"https://pith.science/paper/VZCIXJNI"},"agent_actions":{"view_html":"https://pith.science/pith/VZCIXJNIG6GNWENOSA6HL22DV6","download_json":"https://pith.science/pith/VZCIXJNIG6GNWENOSA6HL22DV6.json","view_paper":"https://pith.science/paper/VZCIXJNI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1301.4144&json=true","fetch_graph":"https://pith.science/api/pith-number/VZCIXJNIG6GNWENOSA6HL22DV6/graph.json","fetch_events":"https://pith.science/api/pith-number/VZCIXJNIG6GNWENOSA6HL22DV6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VZCIXJNIG6GNWENOSA6HL22DV6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VZCIXJNIG6GNWENOSA6HL22DV6/action/storage_attestation","attest_author":"https://pith.science/pith/VZCIXJNIG6GNWENOSA6HL22DV6/action/author_attestation","sign_citation":"https://pith.science/pith/VZCIXJNIG6GNWENOSA6HL22DV6/action/citation_signature","submit_replication":"https://pith.science/pith/VZCIXJNIG6GNWENOSA6HL22DV6/action/replication_record"}},"created_at":"2026-05-18T02:52:12.815685+00:00","updated_at":"2026-05-18T02:52:12.815685+00:00"}