{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001513145,0.0001346333,0.0002520318,0.0001214013,0.00002877094,0.00001264946,0.0001500022,0.00009744116,0.000005202872],"category_scores_gemma":[0.0000127731,0.0001422503,0.0001200694,0.0002515927,0.0001289265,0.000005700857,0.0002236851,0.00005675482,0.000001819741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008963794,"about_ca_system_score_gemma":0.00004843452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003294604,"about_ca_topic_score_gemma":0.000007219835,"domain_scores_codex":[0.9991552,0.00002773556,0.0002625056,0.000283735,0.00008618473,0.000184596],"domain_scores_gemma":[0.9994401,0.000006643326,0.0001591221,0.0002856263,0.00005973799,0.00004877906],"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":[0.00001399742,0.0000324201,0.001651804,0.00006495538,0.0000580236,0.0000027059,0.0001568855,0.0001023189,0.9781883,0.00002496576,0.00002319977,0.01968045],"study_design_scores_gemma":[0.0002317748,0.00006183134,0.001469862,0.000009741065,0.00009767932,0.000002295737,0.0002204529,0.0004306532,0.9942926,0.00002838318,0.003016628,0.0001381253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959062,0.001590369,0.001936629,0.000002342328,0.00001766285,0.0001938187,0.000006559569,0.00001621667,0.0003302094],"genre_scores_gemma":[0.993206,0.001478084,0.004999241,0.000007799248,0.00005286473,0.000005479174,0.00005092356,0.00002749047,0.0001721665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01954232,"threshold_uncertainty_score":0.58008,"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."}}