{"id":"W3189692311","doi":"10.1101/2021.07.29.454377","title":"Virtual screening for small molecule pathway regulators by image profile matching","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Canadian Institutes of Health Research; University of Toronto; Natural Sciences and Engineering Research Council of Canada; Broad Institute; University of Pennsylvania; Division of Civil, Mechanical and Manufacturing Innovation; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Small molecule; Computational biology; Phenotypic screening; Bottleneck; Phenotype; Virtual screening; Gene; Computer science; Biology; Drug discovery; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006970277,0.0007384949,0.0006818977,0.0001712799,0.0002105262,0.0005139547,0.0007900355,0.0008709374,0.00003074627],"category_scores_gemma":[0.0002971304,0.0008749181,0.0005368399,0.0002710696,0.0001348137,0.00001961924,0.001193732,0.0005464139,0.000005745401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000933722,"about_ca_system_score_gemma":0.000465061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006144181,"about_ca_topic_score_gemma":0.000005889278,"domain_scores_codex":[0.9964138,0.0001720202,0.0006611719,0.001742768,0.0003066468,0.0007035578],"domain_scores_gemma":[0.9966054,0.00002926472,0.0005179166,0.001919996,0.0006656607,0.0002617607],"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":[0.00003752327,0.0001201863,0.0001628748,0.0001825408,0.0003228572,0.00002423383,0.000005543433,0.0000209855,0.9954116,0.00003321189,0.003666741,0.00001169873],"study_design_scores_gemma":[0.0003880977,0.0001127044,0.0002794822,0.000199886,0.0001563253,3.909897e-8,0.00001395526,0.0003174187,0.9905702,9.728963e-7,0.006997726,0.0009632271],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7464603,0.001476766,0.2503541,0.00005808278,0.0001368139,0.0009354726,0.0003058467,0.0002493578,0.00002327801],"genre_scores_gemma":[0.9023675,0.0001737144,0.09550475,0.0003221961,0.0004903998,0.0006816012,0.00005539457,0.0003109573,0.00009349428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1559072,"threshold_uncertainty_score":0.9993702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008258833795577769,"score_gpt":0.2215194994615168,"score_spread":0.213260665665939,"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."}}