{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:FY6WP5M2OGZTUS6GHOPAQ5HHSI","short_pith_number":"pith:FY6WP5M2","schema_version":"1.0","canonical_sha256":"2e3d67f59a71b33a4bc63b9e0874e7920078ea24a95c5616deaf2e00dcc0eda1","source":{"kind":"arxiv","id":"1501.03461","version":1},"attestation_state":"computed","paper":{"title":"An Algorithmic Pipeline for Analyzing Multi-parametric Flow Cytometry Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.DS"],"primary_cat":"q-bio.QM","authors_text":"Ariful Azad","submitted_at":"2015-01-14T20:02:13Z","abstract_excerpt":"Flow cytometry (FC) is a single-cell profiling platform for measuring the phenotypes of individual cells from millions of cells in biological samples. FC employs high-throughput technologies and generates high-dimensional data, and hence algorithms for analyzing the data represent a bottleneck. This dissertation addresses several computational challenges arising in modern cytometry while mining information from high-dimensional and high-content biological data. A collection of combinatorial and statistical algorithms for locating, matching, prototyping, and classifying cellular populations fro"},"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":"1501.03461","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2015-01-14T20:02:13Z","cross_cats_sorted":["cs.CE","cs.DS"],"title_canon_sha256":"1d8a2f70d1dc527037c753b391fcf0defc6eab1e4c4c885f53b309ad08bc4b41","abstract_canon_sha256":"2470332cd2c5eacd9c8c4e416da2dfce40a9f62a0b57766c97c99258fcdcd2e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:29:18.318738Z","signature_b64":"NN0UPLCA2peYhgqMwxx/B70jON7bxMZFSfWDVw0e5wKo6o4BqD0mVt1avC5XelVoh9xyEFOn9mjxlBLy6TbaAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e3d67f59a71b33a4bc63b9e0874e7920078ea24a95c5616deaf2e00dcc0eda1","last_reissued_at":"2026-05-18T02:29:18.318328Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:29:18.318328Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Algorithmic Pipeline for Analyzing Multi-parametric Flow Cytometry Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.DS"],"primary_cat":"q-bio.QM","authors_text":"Ariful Azad","submitted_at":"2015-01-14T20:02:13Z","abstract_excerpt":"Flow cytometry (FC) is a single-cell profiling platform for measuring the phenotypes of individual cells from millions of cells in biological samples. FC employs high-throughput technologies and generates high-dimensional data, and hence algorithms for analyzing the data represent a bottleneck. This dissertation addresses several computational challenges arising in modern cytometry while mining information from high-dimensional and high-content biological data. A collection of combinatorial and statistical algorithms for locating, matching, prototyping, and classifying cellular populations fro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.03461","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":"1501.03461","created_at":"2026-05-18T02:29:18.318392+00:00"},{"alias_kind":"arxiv_version","alias_value":"1501.03461v1","created_at":"2026-05-18T02:29:18.318392+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.03461","created_at":"2026-05-18T02:29:18.318392+00:00"},{"alias_kind":"pith_short_12","alias_value":"FY6WP5M2OGZT","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"FY6WP5M2OGZTUS6G","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"FY6WP5M2","created_at":"2026-05-18T12:29:22.688609+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/FY6WP5M2OGZTUS6GHOPAQ5HHSI","json":"https://pith.science/pith/FY6WP5M2OGZTUS6GHOPAQ5HHSI.json","graph_json":"https://pith.science/api/pith-number/FY6WP5M2OGZTUS6GHOPAQ5HHSI/graph.json","events_json":"https://pith.science/api/pith-number/FY6WP5M2OGZTUS6GHOPAQ5HHSI/events.json","paper":"https://pith.science/paper/FY6WP5M2"},"agent_actions":{"view_html":"https://pith.science/pith/FY6WP5M2OGZTUS6GHOPAQ5HHSI","download_json":"https://pith.science/pith/FY6WP5M2OGZTUS6GHOPAQ5HHSI.json","view_paper":"https://pith.science/paper/FY6WP5M2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1501.03461&json=true","fetch_graph":"https://pith.science/api/pith-number/FY6WP5M2OGZTUS6GHOPAQ5HHSI/graph.json","fetch_events":"https://pith.science/api/pith-number/FY6WP5M2OGZTUS6GHOPAQ5HHSI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FY6WP5M2OGZTUS6GHOPAQ5HHSI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FY6WP5M2OGZTUS6GHOPAQ5HHSI/action/storage_attestation","attest_author":"https://pith.science/pith/FY6WP5M2OGZTUS6GHOPAQ5HHSI/action/author_attestation","sign_citation":"https://pith.science/pith/FY6WP5M2OGZTUS6GHOPAQ5HHSI/action/citation_signature","submit_replication":"https://pith.science/pith/FY6WP5M2OGZTUS6GHOPAQ5HHSI/action/replication_record"}},"created_at":"2026-05-18T02:29:18.318392+00:00","updated_at":"2026-05-18T02:29:18.318392+00:00"}