{"id":"W4404342830","doi":"10.1038/s41592-024-02493-2","title":"A comprehensive human embryo reference tool using single-cell RNA-sequencing data","year":2024,"lang":"en","type":"article","venue":"Nature Methods","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal; Hospital for Sick Children; University of Toronto","funders":"Sigrid Juséliuksen Säätiö; Vetenskapsrådet; Knut och Alice Wallenbergs Stiftelse; Canada Research Chairs; Ragnar Söderbergs stiftelse","keywords":"Computational biology; Embryo; Biology; Computer science; Human cell; Benchmarking; Profiling (computer programming); Reference genome; Human genome; Bioinformatics; Genetics; Genome; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005361466,0.0002728549,0.0002531309,0.00008355483,0.0001536418,0.0001456355,0.0006261528,0.0006491856,0.00002911317],"category_scores_gemma":[0.0001210513,0.0002512271,0.0001013274,0.0002145268,0.00009010805,0.00001628369,0.0002967165,0.0007975812,0.000005289626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006070242,"about_ca_system_score_gemma":0.0001723129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006326297,"about_ca_topic_score_gemma":0.00001688182,"domain_scores_codex":[0.9980638,0.0003083897,0.000295411,0.0008226318,0.0001778178,0.0003319208],"domain_scores_gemma":[0.9986621,0.00008374462,0.00006199482,0.000989257,0.0001240093,0.00007886447],"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":[0.00002476804,0.00003427339,0.00002191745,0.0001491896,0.00005559957,0.00002248739,0.00005536275,0.00004417606,0.9863598,0.0001737568,0.0006949187,0.01236376],"study_design_scores_gemma":[0.0002868881,0.0001448976,0.00004217478,0.0001007256,0.00009252737,0.00004771403,0.00006999206,0.004395387,0.8494076,0.0002923669,0.1447158,0.000403967],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8002521,0.02231498,0.169204,0.00006724397,0.001826856,0.000277008,0.0001511063,0.000115407,0.00579123],"genre_scores_gemma":[0.6978275,0.00009500846,0.2995985,0.0005216664,0.0006607943,0.00000367221,0.0005253103,0.00006376628,0.0007037512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1440209,"threshold_uncertainty_score":0.999994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.144216187369988,"score_gpt":0.4054084841681445,"score_spread":0.2611922967981564,"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."}}