{"id":"W3026775944","doi":"10.1177/2472555220917115","title":"High-Content Phenotypic Profiling in Esophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery","year":2020,"lang":"en","type":"article","venue":"SLAS DISCOVERY","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Cancer Research UK; Medical Research Council; University of Pennsylvania","keywords":"Phenotypic screening; High-content screening; Phenotype; Drug repositioning; Drug discovery; Repurposing; Computational biology; Chemical library; Cell; Drug; Biology; Cancer research; Small molecule; Pharmacology; Bioinformatics; Gene; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002220043,0.0004458604,0.000523753,0.000886219,0.0002195161,0.0005002026,0.0002389867,0.0003229617,0.001389764],"category_scores_gemma":[0.0003574982,0.0001720347,0.0004074616,0.0007275483,0.0001873669,0.0002433683,0.0002702778,0.0004072102,0.0004452046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004187996,"about_ca_system_score_gemma":0.0003385084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001522202,"about_ca_topic_score_gemma":0.00349042,"domain_scores_codex":[0.9998177,0.00002458547,0.00001142743,0.00004280057,0.00007583886,0.00002771908],"domain_scores_gemma":[0.99986,0.00003471098,0.00003768815,0.0000211947,0.00002930111,0.00001701068],"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":[0.00009918369,0.00004978871,0.003132351,0.00006988367,0.00002655801,0.00004149522,0.00001415528,0.001534071,0.9807811,0.00007985853,0.0001093277,0.01406224],"study_design_scores_gemma":[0.00001810964,0.0006427162,0.04331277,0.000008182067,0.00007665365,0.0002982713,0.00004149324,0.01000939,0.9433922,0.00009715644,0.00208786,0.00001511428],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9698042,0.002283223,0.02187747,0.0001782284,0.00001052937,0.0001563519,0.002346469,0.000651031,0.002692585],"genre_scores_gemma":[0.9749461,0.001769044,0.01855049,0.0001032088,0.000007055201,0.00008841524,0.002194778,0.00006239939,0.002278395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001522202,"threshold_uncertainty_score":0.004649222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02333521639429506,"score_gpt":0.2796692514022462,"score_spread":0.2563340350079512,"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."}}