{"id":"W2797749376","doi":"10.1016/j.cell.2018.03.040","title":"In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images","year":2018,"lang":"en","type":"article","venue":"Cell","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":680,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Montreal Clinical Research Institute","funders":"National Center for Research Resources; National Institute of General Medical Sciences; National Institute on Aging; National Institute of Neurological Disorders and Stroke; Google","keywords":"Biology; In silico; Fluorescence; Fluorescent labelling; Computational biology; Artificial intelligence; Genetics; Computer science; Gene","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":[0.001190514,0.001620544,0.000978805,0.001032231,0.0005236806,0.001701528,0.001754424,0.00209528,0.003357606],"category_scores_gemma":[0.002310259,0.0008247319,0.001335415,0.0006570898,0.0005933781,0.0009438872,0.0007008066,0.001386734,0.001815525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001147287,"about_ca_system_score_gemma":0.001101526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004239165,"about_ca_topic_score_gemma":0.0103019,"domain_scores_codex":[0.9995029,0.0001306613,0.00002145982,0.0001705483,0.0001185042,0.00005583006],"domain_scores_gemma":[0.9984084,0.001010503,0.0001231233,0.0002297935,0.0001758096,0.00005249654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001576357,0.0008024328,0.01182372,0.0007346892,0.0003370215,0.0006659848,0.000205733,0.2398718,0.4331037,0.009126683,0.007603083,0.2941487],"study_design_scores_gemma":[0.00002364688,0.00009055219,0.0008084209,0.00001770117,0.00006752784,0.000133082,0.0000293286,0.872686,0.1208446,0.003288117,0.001995147,0.0000158925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09109742,0.0002976762,0.8949547,0.0002270409,0.0000537454,0.0001629915,0.001497473,0.0101309,0.001578158],"genre_scores_gemma":[0.3216722,0.0003918453,0.6697293,0.0001980704,0.00003916298,0.0002404363,0.003926022,0.0008688025,0.002934164],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004239165,"threshold_uncertainty_score":0.01123238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005955584155744669,"score_gpt":0.2585947286846439,"score_spread":0.2526391445288993,"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."}}