{"id":"W2463341279","doi":"","title":"Insights into acute myeloid leukemia via single cell network profiling.","year":2010,"lang":"en","type":"article","venue":"PubMed","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency; Princess Margaret Cancer Centre","funders":"","keywords":"Myeloid leukemia; Flow cytometry; Profiling (computer programming); Leukemia; Myeloid; Cell; Computational biology; Mass cytometry; Biology; Cancer research; Computer science; Immunology; Phenotype; Gene; Genetics","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":[0.0002903648,0.0003600329,0.0004361552,0.001048995,0.0001813145,0.0005468305,0.0002727045,0.0002930765,0.0009563327],"category_scores_gemma":[0.0003349466,0.0001584609,0.0002292196,0.0007656362,0.0002714373,0.0009645669,0.0003236629,0.0006101234,0.0002593738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003859709,"about_ca_system_score_gemma":0.0002896724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005162888,"about_ca_topic_score_gemma":0.001182453,"domain_scores_codex":[0.9998823,0.00002062071,0.000006324338,0.00003013263,0.00004583276,0.0000147311],"domain_scores_gemma":[0.9998763,0.00003933953,0.00002803445,0.00001126867,0.00002333641,0.00002172947],"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":[0.0001650567,0.00005050169,0.006178156,0.0007354308,0.00005676915,0.0002535134,0.0001438131,0.006011319,0.8371189,0.008900725,0.002106944,0.1382789],"study_design_scores_gemma":[0.00006513029,0.0007068124,0.08619405,0.0002312188,0.0002728704,0.00359579,0.0006787055,0.2000647,0.5384364,0.06836925,0.1012827,0.0001024512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4479774,0.05487415,0.4686609,0.002992963,0.0003517293,0.000299899,0.007304593,0.001375173,0.0161633],"genre_scores_gemma":[0.8397279,0.03853659,0.1146358,0.0007002304,0.0001841887,0.0002609243,0.002353535,0.00006847856,0.003532317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001048995,"threshold_uncertainty_score":0.00319922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01578078160438932,"score_gpt":0.2436181124757141,"score_spread":0.2278373308713248,"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."}}