{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EB6U3XBEDKEZ6YTMXEACYW3VCI","short_pith_number":"pith:EB6U3XBE","schema_version":"1.0","canonical_sha256":"207d4ddc241a899f626cb9002c5b75121023a31087a0e0fbba54c6341132ef3d","source":{"kind":"arxiv","id":"2405.03726","version":1},"attestation_state":"computed","paper":{"title":"sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.GN","authors_text":"Andac Demir, Bulent Kiziltan, Elizaveta Solovyeva, Fabrizio Serluca, James Boylan, Jeremy Jenkins, Mei Xiao, Murthy Devarakonda, Sebastian Hoersch","submitted_at":"2024-05-06T06:46:11Z","abstract_excerpt":"Influenced by breakthroughs in LLMs, single-cell foundation models are emerging. While these models show successful performance in cell type clustering, phenotype classification, and gene perturbation response prediction, it remains to be seen if a simpler model could achieve comparable or better results, especially with limited data. This is important, as the quantity and quality of single-cell data typically fall short of the standards in textual data used for training LLMs. Single-cell sequencing often suffers from technical artifacts, dropout events, and batch effects. These challenges are"},"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":"2405.03726","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-bio.GN","submitted_at":"2024-05-06T06:46:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1b62f987653ab4f0ec2e87749f43c319d594f739d4afbab05b9bd1c98804a529","abstract_canon_sha256":"3f852939f4e89f6083f9e63b7cbfc2fa5afdd478adb56034c81f0217ca2f4e24"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:16:09.996609Z","signature_b64":"k37+NWKjf1H3a9GvGpACtDAoQBAEb4uAeUkCYAMAP00JFYMrIbgIvQniTAQKUnSOBWPszVdSeLMGcx0yxr/ODA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"207d4ddc241a899f626cb9002c5b75121023a31087a0e0fbba54c6341132ef3d","last_reissued_at":"2026-07-05T08:16:09.996217Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:16:09.996217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.GN","authors_text":"Andac Demir, Bulent Kiziltan, Elizaveta Solovyeva, Fabrizio Serluca, James Boylan, Jeremy Jenkins, Mei Xiao, Murthy Devarakonda, Sebastian Hoersch","submitted_at":"2024-05-06T06:46:11Z","abstract_excerpt":"Influenced by breakthroughs in LLMs, single-cell foundation models are emerging. While these models show successful performance in cell type clustering, phenotype classification, and gene perturbation response prediction, it remains to be seen if a simpler model could achieve comparable or better results, especially with limited data. This is important, as the quantity and quality of single-cell data typically fall short of the standards in textual data used for training LLMs. Single-cell sequencing often suffers from technical artifacts, dropout events, and batch effects. These challenges are"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.03726","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.03726/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":"2405.03726","created_at":"2026-07-05T08:16:09.996269+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.03726v1","created_at":"2026-07-05T08:16:09.996269+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.03726","created_at":"2026-07-05T08:16:09.996269+00:00"},{"alias_kind":"pith_short_12","alias_value":"EB6U3XBEDKEZ","created_at":"2026-07-05T08:16:09.996269+00:00"},{"alias_kind":"pith_short_16","alias_value":"EB6U3XBEDKEZ6YTM","created_at":"2026-07-05T08:16:09.996269+00:00"},{"alias_kind":"pith_short_8","alias_value":"EB6U3XBE","created_at":"2026-07-05T08:16:09.996269+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.08303","citing_title":"Evaluation of an Autonomous Surface Robot Equipped with a Transformable Mobility Mechanism for Efficient Mobility Control","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EB6U3XBEDKEZ6YTMXEACYW3VCI","json":"https://pith.science/pith/EB6U3XBEDKEZ6YTMXEACYW3VCI.json","graph_json":"https://pith.science/api/pith-number/EB6U3XBEDKEZ6YTMXEACYW3VCI/graph.json","events_json":"https://pith.science/api/pith-number/EB6U3XBEDKEZ6YTMXEACYW3VCI/events.json","paper":"https://pith.science/paper/EB6U3XBE"},"agent_actions":{"view_html":"https://pith.science/pith/EB6U3XBEDKEZ6YTMXEACYW3VCI","download_json":"https://pith.science/pith/EB6U3XBEDKEZ6YTMXEACYW3VCI.json","view_paper":"https://pith.science/paper/EB6U3XBE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.03726&json=true","fetch_graph":"https://pith.science/api/pith-number/EB6U3XBEDKEZ6YTMXEACYW3VCI/graph.json","fetch_events":"https://pith.science/api/pith-number/EB6U3XBEDKEZ6YTMXEACYW3VCI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EB6U3XBEDKEZ6YTMXEACYW3VCI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EB6U3XBEDKEZ6YTMXEACYW3VCI/action/storage_attestation","attest_author":"https://pith.science/pith/EB6U3XBEDKEZ6YTMXEACYW3VCI/action/author_attestation","sign_citation":"https://pith.science/pith/EB6U3XBEDKEZ6YTMXEACYW3VCI/action/citation_signature","submit_replication":"https://pith.science/pith/EB6U3XBEDKEZ6YTMXEACYW3VCI/action/replication_record"}},"created_at":"2026-07-05T08:16:09.996269+00:00","updated_at":"2026-07-05T08:16:09.996269+00:00"}