{"id":"W2017222169","doi":"10.1186/1758-2946-6-1","title":"Prediction of novel drug indications using network driven biological data prioritization and integration","year":2014,"lang":"en","type":"article","venue":"Journal of Cheminformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Computer science; Prioritization; Data integration; Data science; Data mining; Drug; Risk analysis (engineering); Management science; Pharmacology; Medicine; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009391261,0.001067042,0.0007807833,0.005211205,0.0003231046,0.001044384,0.0005971267,0.0006493411,0.002831665],"category_scores_gemma":[0.004033083,0.000269949,0.001335791,0.002188485,0.0002334154,0.0006004302,0.000522902,0.0006656002,0.0004859405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001277428,"about_ca_system_score_gemma":0.00172178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009137061,"about_ca_topic_score_gemma":0.01316225,"domain_scores_codex":[0.9996154,0.0001084021,0.00004072662,0.00009852276,0.0001029896,0.00003390029],"domain_scores_gemma":[0.9976042,0.001600483,0.000325325,0.00006338611,0.0002997023,0.0001069557],"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":[0.001055236,0.0004942885,0.04992553,0.0005253039,0.0004340345,0.0005860429,0.00005850699,0.7503597,0.008991825,0.002989293,0.004195988,0.1803842],"study_design_scores_gemma":[0.000025647,0.0000654011,0.002414132,0.00001488356,0.00006974218,0.00008031511,0.00001081623,0.9926923,0.001505651,0.00244805,0.0006643,0.000008787567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3725978,0.002309682,0.5968478,0.002044018,0.00009099694,0.0007734666,0.01548496,0.00603311,0.003818168],"genre_scores_gemma":[0.7894008,0.0007519096,0.1964418,0.0002462596,0.00007640159,0.000372614,0.0115798,0.0001170614,0.001013344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009137061,"threshold_uncertainty_score":0.01816773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03665510091314504,"score_gpt":0.2588013996024858,"score_spread":0.2221462986893407,"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."}}