{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5DTUTYUD4BCOTILV5EAUMU3U7U","short_pith_number":"pith:5DTUTYUD","schema_version":"1.0","canonical_sha256":"e8e749e283e044e9a175e901465374fd3cb081f878481077e5fc959c4450f4c3","source":{"kind":"arxiv","id":"2408.10689","version":2},"attestation_state":"computed","paper":{"title":"Genesis: Towards the Automation of Systems Biology Research","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Alexander H. Gower, Daniel Brunns{\\aa}ker, Erik Y. Bjurstr\\\"om, Filip Kronstr\\\"om, Gabriel K. Reder, Ievgeniia A. Tiukova, John P. Wikswo, Konstantin Korovin, Larisa B. Soldatova, Ronald S. Reiserer, Ross D. King","submitted_at":"2024-08-20T09:40:43Z","abstract_excerpt":"The cutting edge of applying AI to science is the closed-loop automation of scientific research: robot scientists. We have previously developed two robot scientists: `Adam' (for yeast functional biology), and `Eve' (for early-stage drug design)). We are now developing a next generation robot scientist Genesis. With Genesis we aim to demonstrate that an area of science can be investigated using robot scientists unambiguously faster, and at lower cost, than with human scientists. Here we report progress on the Genesis project. Genesis is designed to automatically improve system biology models wi"},"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":"2408.10689","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-08-20T09:40:43Z","cross_cats_sorted":[],"title_canon_sha256":"06dc61b1d21a727f76cc1766f40a1ed04802886c3df3844cc57695a8b102ab1d","abstract_canon_sha256":"e0f96ae370d54364904a32dbb2e4bb64b12c2b05a732a13a88da05687745b4cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:03:02.270149Z","signature_b64":"N0J2TT2rsadDwmn4qE9cDoFfTQqTmswZYD99P+Mz5hV1EUeoLrCxKGIhJLHGj4OgT1+UsoyPyY54H1VPdlgMCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8e749e283e044e9a175e901465374fd3cb081f878481077e5fc959c4450f4c3","last_reissued_at":"2026-07-05T09:03:02.269654Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:03:02.269654Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Genesis: Towards the Automation of Systems Biology Research","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Alexander H. Gower, Daniel Brunns{\\aa}ker, Erik Y. Bjurstr\\\"om, Filip Kronstr\\\"om, Gabriel K. Reder, Ievgeniia A. Tiukova, John P. Wikswo, Konstantin Korovin, Larisa B. Soldatova, Ronald S. Reiserer, Ross D. King","submitted_at":"2024-08-20T09:40:43Z","abstract_excerpt":"The cutting edge of applying AI to science is the closed-loop automation of scientific research: robot scientists. We have previously developed two robot scientists: `Adam' (for yeast functional biology), and `Eve' (for early-stage drug design)). We are now developing a next generation robot scientist Genesis. With Genesis we aim to demonstrate that an area of science can be investigated using robot scientists unambiguously faster, and at lower cost, than with human scientists. Here we report progress on the Genesis project. Genesis is designed to automatically improve system biology models wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.10689","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2408.10689/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2408.10689","created_at":"2026-07-05T09:03:02.269718+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.10689v2","created_at":"2026-07-05T09:03:02.269718+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.10689","created_at":"2026-07-05T09:03:02.269718+00:00"},{"alias_kind":"pith_short_12","alias_value":"5DTUTYUD4BCO","created_at":"2026-07-05T09:03:02.269718+00:00"},{"alias_kind":"pith_short_16","alias_value":"5DTUTYUD4BCOTILV","created_at":"2026-07-05T09:03:02.269718+00:00"},{"alias_kind":"pith_short_8","alias_value":"5DTUTYUD","created_at":"2026-07-05T09:03:02.269718+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.23204","citing_title":"AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery","ref_index":159,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5DTUTYUD4BCOTILV5EAUMU3U7U","json":"https://pith.science/pith/5DTUTYUD4BCOTILV5EAUMU3U7U.json","graph_json":"https://pith.science/api/pith-number/5DTUTYUD4BCOTILV5EAUMU3U7U/graph.json","events_json":"https://pith.science/api/pith-number/5DTUTYUD4BCOTILV5EAUMU3U7U/events.json","paper":"https://pith.science/paper/5DTUTYUD"},"agent_actions":{"view_html":"https://pith.science/pith/5DTUTYUD4BCOTILV5EAUMU3U7U","download_json":"https://pith.science/pith/5DTUTYUD4BCOTILV5EAUMU3U7U.json","view_paper":"https://pith.science/paper/5DTUTYUD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.10689&json=true","fetch_graph":"https://pith.science/api/pith-number/5DTUTYUD4BCOTILV5EAUMU3U7U/graph.json","fetch_events":"https://pith.science/api/pith-number/5DTUTYUD4BCOTILV5EAUMU3U7U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5DTUTYUD4BCOTILV5EAUMU3U7U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5DTUTYUD4BCOTILV5EAUMU3U7U/action/storage_attestation","attest_author":"https://pith.science/pith/5DTUTYUD4BCOTILV5EAUMU3U7U/action/author_attestation","sign_citation":"https://pith.science/pith/5DTUTYUD4BCOTILV5EAUMU3U7U/action/citation_signature","submit_replication":"https://pith.science/pith/5DTUTYUD4BCOTILV5EAUMU3U7U/action/replication_record"}},"created_at":"2026-07-05T09:03:02.269718+00:00","updated_at":"2026-07-05T09:03:02.269718+00:00"}