{"id":"W2117628101","doi":"10.1093/bioinformatics/btu807","title":"flowCL: ontology-based cell population labelling in flow cytometry","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences","keywords":"Labelling; Flow cytometry; Computer science; Ontology; Population; Biology; Molecular biology; Medicine; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007830508,0.002941396,0.001673606,0.006378162,0.002222724,0.004674802,0.004195989,0.002427805,0.02089469],"category_scores_gemma":[0.01144943,0.002104984,0.00361415,0.003856629,0.001929615,0.004668503,0.004732368,0.003615544,0.01334416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003424458,"about_ca_system_score_gemma":0.005993267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007201226,"about_ca_topic_score_gemma":0.007400723,"domain_scores_codex":[0.9969544,0.0004754602,0.0004671904,0.0008589085,0.001023587,0.0002203857],"domain_scores_gemma":[0.9939536,0.003056039,0.0006582864,0.001143302,0.0008897913,0.0002988615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001532951,0.0005133039,0.01352108,0.008991078,0.001072948,0.001377262,0.003022,0.02396336,0.08961491,0.07628686,0.4105208,0.3695834],"study_design_scores_gemma":[0.0004679053,0.0002391884,0.008786505,0.001284321,0.0003889562,0.001238362,0.0004346807,0.1583282,0.06725743,0.12556,0.6355326,0.0004818794],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002301044,0.0002747066,0.7827199,0.0003970765,0.0001774783,0.0007344607,0.02519054,0.1848954,0.0033095],"genre_scores_gemma":[0.02369636,0.0008279973,0.8450109,0.001030199,0.0001166029,0.004849426,0.08707927,0.03247074,0.004918497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02089469,"threshold_uncertainty_score":0.06989968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009206576639586,"score_gpt":0.2153353101133954,"score_spread":0.2061287334738094,"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."}}