{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007620339,0.001107824,0.001266557,0.001201847,0.0004602324,0.001009795,0.0007269931,0.0008386824,0.00402687],"category_scores_gemma":[0.0007150743,0.0004833593,0.001426138,0.0005092256,0.0004284645,0.000580682,0.0008567293,0.001243776,0.00152726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001215181,"about_ca_system_score_gemma":0.001787349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003261942,"about_ca_topic_score_gemma":0.00856288,"domain_scores_codex":[0.9996173,0.00003058348,0.00002261815,0.00008713429,0.000190019,0.00005221737],"domain_scores_gemma":[0.9997631,0.00005166786,0.0000459924,0.00002772994,0.00007325492,0.00003822439],"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":[0.0003126034,0.0001305137,0.001361322,0.0005852399,0.0001118429,0.0003368036,0.00004496724,0.007915188,0.9513177,0.001347109,0.001815133,0.03472167],"study_design_scores_gemma":[0.0002019585,0.003097479,0.005155992,0.0001182165,0.0004944793,0.0006832736,0.00007933549,0.04043989,0.9190205,0.001431475,0.02913455,0.0001429209],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6525768,0.0112855,0.2683141,0.002623284,0.000332829,0.004103203,0.02282159,0.01270228,0.02524049],"genre_scores_gemma":[0.7147142,0.01003002,0.2474124,0.001076496,0.000042436,0.00149953,0.011854,0.0007549926,0.01261584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00402687,"threshold_uncertainty_score":0.01347119,"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."}}