{"id":"W4409728004","doi":"10.1101/2025.04.11.648220","title":"CellFlow enables generative single-cell phenotype modeling with flow matching","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Matching (statistics); Generative grammar; Phenotype; Computer science; Flow (mathematics); Generative model; Artificial intelligence; Mathematics; Biology; Genetics; Geometry; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003053276,0.0008526177,0.0006397127,0.0001808492,0.0002857842,0.0002800534,0.0006892285,0.000841765,0.00001645758],"category_scores_gemma":[0.00004123129,0.0008493949,0.0002265337,0.0002610244,0.0001108205,0.00001544271,0.0004453112,0.0007324751,0.00001099283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001381372,"about_ca_system_score_gemma":0.0008731582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001147497,"about_ca_topic_score_gemma":0.00003197795,"domain_scores_codex":[0.9967649,0.0001532777,0.0005511257,0.001480242,0.0003420997,0.000708346],"domain_scores_gemma":[0.9976792,0.00002259059,0.0002627541,0.00129494,0.0005004077,0.0002401533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001701092,0.0002783552,0.0002728377,0.0004530849,0.0002177148,0.00002074029,0.00002628755,0.06498963,0.9333627,0.00006163037,0.0001420587,0.000004832899],"study_design_scores_gemma":[0.0009672703,0.0002204057,0.00006157837,0.0004519378,0.0002190205,2.591206e-8,0.00001056855,0.03029032,0.9645464,0.000006358553,0.001994438,0.001231627],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.740827,0.002759194,0.2539885,0.00008875703,0.001045014,0.0006349193,0.0003386837,0.0001719836,0.0001459967],"genre_scores_gemma":[0.9398249,0.0003858146,0.05819206,0.0003960666,0.0008580617,0.0001074854,0.00000859529,0.000165682,0.00006133084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1989979,"threshold_uncertainty_score":0.9993957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01375887168781217,"score_gpt":0.1976894805154835,"score_spread":0.1839306088276713,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}