{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:HGSX6FRQWNGRV4LU5GGI5PBBRZ","short_pith_number":"pith:HGSX6FRQ","schema_version":"1.0","canonical_sha256":"39a57f1630b34d1af174e98c8ebc218e47b7e160b121f490579c6c29372762b1","source":{"kind":"arxiv","id":"1411.0595","version":1},"attestation_state":"computed","paper":{"title":"Reverse enGENEering of regulatory networks from Big Data: a guide for a biologist","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.MN","authors_text":"Anatoly Yambartsev, Andrey Morgun, Lina Thomas, Natalia Shulzhenko, Stephen Ramsey, Xiaoxi Dong","submitted_at":"2014-11-03T18:23:17Z","abstract_excerpt":"Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform this data into biological knowledge. For example, how to use this data to answer questions such as: which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network rec"},"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":"1411.0595","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.MN","submitted_at":"2014-11-03T18:23:17Z","cross_cats_sorted":[],"title_canon_sha256":"f93cf093ff52675d046d808ec11dce70d411b5b72880ecd591fe0e665bd7567a","abstract_canon_sha256":"1b81a7aec1bcb8d0ef8d041e80bb1c20902e469d106b378ada4c70c4a4c66a8c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:38:45.049463Z","signature_b64":"M1bthuDyU5Dn7214Rh+h1fdCGmA4bG+t4dPQcSgmYhiVeUQO2yKLJ0i94W2MnHOmK7VEHyEC0E+PNH13Ue2nAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39a57f1630b34d1af174e98c8ebc218e47b7e160b121f490579c6c29372762b1","last_reissued_at":"2026-05-18T02:38:45.048949Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:38:45.048949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reverse enGENEering of regulatory networks from Big Data: a guide for a biologist","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.MN","authors_text":"Anatoly Yambartsev, Andrey Morgun, Lina Thomas, Natalia Shulzhenko, Stephen Ramsey, Xiaoxi Dong","submitted_at":"2014-11-03T18:23:17Z","abstract_excerpt":"Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform this data into biological knowledge. For example, how to use this data to answer questions such as: which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network rec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.0595","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":"1411.0595","created_at":"2026-05-18T02:38:45.049024+00:00"},{"alias_kind":"arxiv_version","alias_value":"1411.0595v1","created_at":"2026-05-18T02:38:45.049024+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.0595","created_at":"2026-05-18T02:38:45.049024+00:00"},{"alias_kind":"pith_short_12","alias_value":"HGSX6FRQWNGR","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_16","alias_value":"HGSX6FRQWNGRV4LU","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_8","alias_value":"HGSX6FRQ","created_at":"2026-05-18T12:28:30.664211+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/HGSX6FRQWNGRV4LU5GGI5PBBRZ","json":"https://pith.science/pith/HGSX6FRQWNGRV4LU5GGI5PBBRZ.json","graph_json":"https://pith.science/api/pith-number/HGSX6FRQWNGRV4LU5GGI5PBBRZ/graph.json","events_json":"https://pith.science/api/pith-number/HGSX6FRQWNGRV4LU5GGI5PBBRZ/events.json","paper":"https://pith.science/paper/HGSX6FRQ"},"agent_actions":{"view_html":"https://pith.science/pith/HGSX6FRQWNGRV4LU5GGI5PBBRZ","download_json":"https://pith.science/pith/HGSX6FRQWNGRV4LU5GGI5PBBRZ.json","view_paper":"https://pith.science/paper/HGSX6FRQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1411.0595&json=true","fetch_graph":"https://pith.science/api/pith-number/HGSX6FRQWNGRV4LU5GGI5PBBRZ/graph.json","fetch_events":"https://pith.science/api/pith-number/HGSX6FRQWNGRV4LU5GGI5PBBRZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HGSX6FRQWNGRV4LU5GGI5PBBRZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HGSX6FRQWNGRV4LU5GGI5PBBRZ/action/storage_attestation","attest_author":"https://pith.science/pith/HGSX6FRQWNGRV4LU5GGI5PBBRZ/action/author_attestation","sign_citation":"https://pith.science/pith/HGSX6FRQWNGRV4LU5GGI5PBBRZ/action/citation_signature","submit_replication":"https://pith.science/pith/HGSX6FRQWNGRV4LU5GGI5PBBRZ/action/replication_record"}},"created_at":"2026-05-18T02:38:45.049024+00:00","updated_at":"2026-05-18T02:38:45.049024+00:00"}