{"id":"W4294275214","doi":"10.1016/j.cels.2022.08.003","title":"Virtual screening for small-molecule pathway regulators by image-profile matching","year":2022,"lang":"en","type":"article","venue":"Cell Systems","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of General Medical Sciences; National Heart, Lung, and Blood Institute; National Cancer Institute; University of Toronto; National Institutes of Health; Canada Research Chairs; Bayer Fund; Natural Sciences and Engineering Research Council of Canada; Broad Institute; University of Pennsylvania; National Science Foundation; Canadian Institutes of Health Research; American Cancer Society","keywords":"Computational biology; YAP1; Phenotypic screening; Biology; Small molecule; Phenotype; Gene; Virtual screening; Bottleneck; Drug discovery; Phenocopy; Computer science; Bioinformatics; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000396478,0.001103069,0.001020452,0.0009294088,0.0002813468,0.0008407247,0.0008351062,0.0004806554,0.002635993],"category_scores_gemma":[0.0007070259,0.0003043593,0.001118824,0.0008564563,0.0002153086,0.0004914981,0.0007338854,0.0005186104,0.0007802567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005061646,"about_ca_system_score_gemma":0.0007397936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009579535,"about_ca_topic_score_gemma":0.001081902,"domain_scores_codex":[0.9996872,0.00005212886,0.00001480175,0.00007582014,0.0001253179,0.00004477131],"domain_scores_gemma":[0.9998583,0.00005552246,0.00002073097,0.00002392972,0.00002509574,0.00001634672],"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":[0.001997277,0.0006186102,0.003298578,0.0007091211,0.0003709249,0.0004344444,0.00004959207,0.1244059,0.627915,0.005669774,0.003780762,0.23075],"study_design_scores_gemma":[0.0001098241,0.0004826346,0.002014195,0.00001276966,0.0001864501,0.0002762983,0.00003168248,0.6497725,0.3389156,0.002891784,0.005263014,0.00004321216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4702565,0.001801464,0.5019452,0.0002769828,0.00007791527,0.0006615419,0.003640206,0.0117825,0.009557648],"genre_scores_gemma":[0.8513638,0.000991152,0.1409142,0.0001136982,0.00001309331,0.0003457552,0.003809343,0.0002697185,0.002179301],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002635993,"threshold_uncertainty_score":0.008818269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0074553833574173,"score_gpt":0.2242782167792864,"score_spread":0.2168228334218691,"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."}}