{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005312408,0.0002009604,0.0002328904,0.00006571072,0.0003249352,0.00009914291,0.0003749712,0.00009150785,0.00004257401],"category_scores_gemma":[0.00002242586,0.0002219844,0.0002073504,0.0001251901,0.00003736819,0.000005342273,0.0003225335,0.0001272843,0.000006111182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003958256,"about_ca_system_score_gemma":0.00004442249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001378451,"about_ca_topic_score_gemma":0.000003482901,"domain_scores_codex":[0.9984733,0.0001528871,0.0003270316,0.000525663,0.000193671,0.0003274814],"domain_scores_gemma":[0.9990858,0.00002030712,0.000194474,0.0005382833,0.00007754339,0.00008361606],"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":[0.00002995301,0.00004601732,0.00004034182,0.00003683659,0.00004337745,0.000004044124,0.00004338119,0.0001393549,0.9441531,0.000035227,0.05490002,0.0005282952],"study_design_scores_gemma":[0.0003458324,0.0002664277,0.000002493058,0.00000796481,0.00002898304,0.00001215654,0.0008841699,0.001232054,0.8635437,0.0000080461,0.1333884,0.0002797265],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.697734,0.002452388,0.2913419,0.00003689809,0.0001963126,0.001134681,0.000175142,0.0001466154,0.006782099],"genre_scores_gemma":[0.9784257,0.00001089302,0.002381891,0.0001211487,0.0001994939,0.0005569425,0.001168024,0.0000801015,0.01705574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.28896,"threshold_uncertainty_score":0.9052259,"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."}}