{"id":"W2145274698","doi":"10.1155/2009/584603","title":"A Survey of Flow Cytometry Data Analysis Methods","year":2009,"lang":"en","type":"article","venue":"Advances in Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Michael Smith Health Research BC","keywords":"Computer science; Pipeline (software); Limiting; Flow cytometry; Data mining; Medicine; Immunology","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":[],"consensus_categories":[],"category_scores_codex":[0.000833753,0.0001213719,0.0002765764,0.0001778569,0.00002252531,0.00001246697,0.0005078106,0.0001010857,0.000005945071],"category_scores_gemma":[0.0003221179,0.0001099559,0.00006243584,0.0008832357,0.00005658716,0.00003123751,0.0000805084,0.0000769035,0.000001277352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008765893,"about_ca_system_score_gemma":0.00003652954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004102943,"about_ca_topic_score_gemma":0.0004574204,"domain_scores_codex":[0.9989772,0.00007828138,0.000475624,0.0001725488,0.0001190338,0.0001772988],"domain_scores_gemma":[0.9989149,0.00005332562,0.0001483597,0.0007666278,0.00007420326,0.00004259161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002296915,0.000324709,0.1971987,0.0001356113,0.0003435987,0.000001652782,0.0003097097,0.01112776,0.0246611,0.00004697183,0.000223233,0.7653973],"study_design_scores_gemma":[0.001676566,0.0007832883,0.2975182,0.00005162609,0.0003283509,0.000005974329,0.0002035303,0.6072174,0.07484055,0.0002626692,0.01627995,0.0008319311],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08263421,0.005135168,0.9094982,0.00001998169,0.0001720826,0.0001333715,0.0003364417,0.00001167015,0.002058877],"genre_scores_gemma":[0.6242203,0.002093364,0.3721367,0.0001591602,0.00002104944,9.697993e-7,0.001343534,0.000005605548,0.00001935324],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7645653,"threshold_uncertainty_score":0.4483872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03930544295823368,"score_gpt":0.3698862213147036,"score_spread":0.3305807783564699,"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."}}