{"id":"W4372295140","doi":"10.15252/emmm.202216267","title":"A multilevel screening pipeline in zebrafish identifies therapeutic drugs for GAN","year":2023,"lang":"en","type":"article","venue":"EMBO Molecular Medicine","topic":"Cellular Mechanics and Interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"College of Natural Resources and Sciences, Humboldt State University; Centre National de la Recherche Scientifique; Université de Lyon; Fondation pour la Recherche Médicale; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche; Muscular Dystrophy Association; Fondation Maladies Rares; French Muscular Dystrophy Association; Advanced Foods and Materials Canada","keywords":"Zebrafish; Neuroscience; Postsynaptic potential; In silico; Motility; Pipeline (software); Neuromuscular junction; Biology; Drug; Computational biology; Medicine; Bioinformatics; Computer science; Cell biology; Pharmacology; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000454185,0.0001830971,0.0002192945,0.0002025025,0.00007536162,0.00001716735,0.0001962147,0.0001182679,0.00006269984],"category_scores_gemma":[0.0002612095,0.0001706914,0.0001207049,0.0002615312,0.0000509642,0.000005203276,0.00006893706,0.0001243997,0.00001701734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000123659,"about_ca_system_score_gemma":0.0000312706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005502787,"about_ca_topic_score_gemma":0.00004610919,"domain_scores_codex":[0.9986976,0.00005063216,0.0003238441,0.0004025786,0.000189419,0.000335883],"domain_scores_gemma":[0.9993395,0.00003676282,0.00007739846,0.0003572253,0.0001057436,0.00008342593],"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":[0.00005449314,0.00003270635,0.00002261863,0.00002464349,0.00004500414,0.0000162221,0.0002048188,0.0001134503,0.9875491,0.000368667,0.005717083,0.005851204],"study_design_scores_gemma":[0.003454066,0.0004172151,0.0007229578,0.0001855653,0.00007284702,0.00002074162,0.001337804,0.02731838,0.8350385,0.001833578,0.1291756,0.00042272],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5221307,0.002346919,0.4660309,0.004991991,0.001141354,0.001050471,0.00004295774,0.0001043622,0.0021604],"genre_scores_gemma":[0.9941296,0.0001230504,0.0004172758,0.001447127,0.0003096332,0.0001114351,0.0006519562,0.00005350612,0.002756442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4719989,"threshold_uncertainty_score":0.6960593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02350539524363854,"score_gpt":0.3066725589823636,"score_spread":0.2831671637387251,"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."}}