{"id":"W4381432912","doi":"10.1016/j.xgen.2023.100346","title":"Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler","year":2023,"lang":"en","type":"article","venue":"Cell Genomics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institutes of Health; Joslin Diabetes Center; Universität Ulm; Novo Nordisk Fonden; Doris Duke Charitable Foundation; Else Kröner-Fresenius-Stiftung; Novo Nordisk; Harvard Medical School; National Institute of Diabetes and Digestive and Kidney Diseases; Bill and Melinda Gates Foundation; Li Ka Shing Foundation; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Broad Institute; NIHR Oxford Biomedical Research Centre; Wellcome Trust; American Diabetes Association","keywords":"Biology; Computational biology; Disease; Phenotype; Profiling (computer programming); Quantitative trait locus; Context (archaeology); Effector; Genetics; Gene; Bioinformatics; Computer science; Medicine; Cell biology; Pathology","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":[0.0006867052,0.0003986519,0.0004027245,0.0008646157,0.0002817151,0.0008441294,0.0003575299,0.0005056961,0.001913772],"category_scores_gemma":[0.0007986595,0.0003213085,0.0004090842,0.0003273907,0.0003034854,0.0004212561,0.0005897437,0.0007698939,0.0005522213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002882731,"about_ca_system_score_gemma":0.0003318951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005070342,"about_ca_topic_score_gemma":0.001426455,"domain_scores_codex":[0.9997937,0.00003481281,0.00001161872,0.0000704103,0.00005784099,0.00003176317],"domain_scores_gemma":[0.9996202,0.000155324,0.00008083667,0.00005977879,0.00003566934,0.00004821567],"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":[0.0006086031,0.00008542173,0.0291287,0.0004147037,0.000158681,0.0003278037,0.0002743969,0.007106777,0.9067344,0.003320875,0.00350784,0.04833184],"study_design_scores_gemma":[0.00007507351,0.0002981632,0.057899,0.00009072571,0.0001572728,0.0009617271,0.0002637917,0.1802109,0.7351245,0.005105142,0.01971723,0.00009648137],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6741207,0.0010435,0.3005348,0.0003445642,0.00008483289,0.0001387555,0.007566761,0.01292147,0.00324453],"genre_scores_gemma":[0.8245816,0.0007247952,0.1646769,0.0003401624,0.00002168869,0.0004261578,0.005163476,0.001551671,0.002513509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001913772,"threshold_uncertainty_score":0.006402254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01347712201397075,"score_gpt":0.2351952809039814,"score_spread":0.2217181588900106,"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."}}