{"id":"W4389786332","doi":"10.1101/2023.12.14.571727","title":"A high-throughput screening approach to discover potential colorectal cancer chemotherapeutics: Repurposing drugs to disrupt 14-3-3 protein-BAD interactions","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"14-3-3 protein interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Medical Research Council; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health; UK Research and Innovation; Wellcome Trust; National Institute of Diabetes and Digestive and Kidney Diseases; McGill University","keywords":"Apoptosis; Colorectal cancer; Drug repositioning; Cancer; Cancer research; Mechanism (biology); Programmed cell death; Cell; Cancer cell; Repurposing; Drug discovery; Drug; Biology; Pharmacology; Bioinformatics; Biochemistry; 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":[0.0004399663,0.0009220738,0.0007001127,0.0004204187,0.0004279737,0.0005509941,0.0009413022,0.0006620834,0.00004304296],"category_scores_gemma":[0.0003036051,0.00104195,0.000393208,0.0007258053,0.0001279652,0.00004343126,0.001720785,0.001090688,0.0001248505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004772322,"about_ca_system_score_gemma":0.0006813456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001326305,"about_ca_topic_score_gemma":0.00008154781,"domain_scores_codex":[0.9951628,0.0002000322,0.0008141881,0.002265788,0.0005677502,0.0009893677],"domain_scores_gemma":[0.9966031,0.00002739841,0.0004804922,0.001665124,0.0007040159,0.0005198811],"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":[0.0003205991,0.0002563484,0.0003362697,0.0001575868,0.0006685287,0.00001410736,0.00006759328,0.005226245,0.9914257,0.0000670713,0.001430102,0.0000298377],"study_design_scores_gemma":[0.0006011797,0.0001836911,0.007794479,0.0008055757,0.0002103263,2.328836e-7,0.00003476941,0.0005112137,0.9688715,0.000003168191,0.01954704,0.001436771],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.902816,0.0002891839,0.08830094,0.001105382,0.002697746,0.003459989,0.0009264771,0.0003795811,0.00002472665],"genre_scores_gemma":[0.9469934,0.00005633382,0.04500049,0.0006227834,0.002180158,0.004435211,0.00001282326,0.0004258023,0.0002729741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04417745,"threshold_uncertainty_score":0.9992031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01554955335218689,"score_gpt":0.2645531972113688,"score_spread":0.2490036438591819,"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."}}