{"id":"W1899644141","doi":"10.1002/minf.201400118","title":"Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set","year":2015,"lang":"en","type":"article","venue":"Molecular Informatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Linear discriminant analysis; Set (abstract data type); Test set; Computer science; Data set; Artificial intelligence; Machine learning; Regression; Linear regression; Data mining; Variance (accounting); Drug discovery; Linear model; Regression analysis; Statistics; Mathematics; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001932814,0.001588716,0.001562584,0.002191075,0.0004559981,0.001673984,0.001251606,0.001393962,0.001265916],"category_scores_gemma":[0.006421445,0.0003589928,0.001657236,0.001885738,0.0005068915,0.001274628,0.001018279,0.001664039,0.0007756833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007916277,"about_ca_system_score_gemma":0.00179875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005324489,"about_ca_topic_score_gemma":0.005974148,"domain_scores_codex":[0.9991544,0.0002598593,0.00008372868,0.000244606,0.000202406,0.000054939],"domain_scores_gemma":[0.9965064,0.001954121,0.0003294087,0.00055156,0.0005653698,0.00009317879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008530105,0.001480678,0.02325714,0.0025652,0.0006024166,0.001225905,0.0002426858,0.7050898,0.0803821,0.003314148,0.01091916,0.1700677],"study_design_scores_gemma":[0.00005414704,0.0006672733,0.01023284,0.0001145375,0.0002046267,0.0004492345,0.0001364091,0.9257069,0.04501997,0.003478725,0.01381164,0.0001236775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.50233,0.0041374,0.4572844,0.001070269,0.0002092698,0.000593992,0.026302,0.004894157,0.003178488],"genre_scores_gemma":[0.6890848,0.002329833,0.2540661,0.0003126124,0.00008475394,0.0008452006,0.05112789,0.000326255,0.001822436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005324489,"threshold_uncertainty_score":0.01058698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1242311728463903,"score_gpt":0.3528909864422495,"score_spread":0.2286598135958592,"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."}}