{"id":"W4296462700","doi":"10.18433/jpps.v19i4.28245","title":"Predicting High-Impact Pharmacological Targets by Integrating Transcriptome and Text-Mining Features","year":2016,"lang":"en","type":"article","venue":"Journal of Pharmacy & Pharmaceutical Sciences","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Drug repositioning; Repurposing; Transcriptome; Computational biology; Computer science; Interpretability; Data mining; Bioinformatics; Biology; Machine learning; Gene; Gene expression; Drug; Pharmacology; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001106179,0.0006638242,0.0007599164,0.006707818,0.0002651062,0.00154231,0.0003141554,0.000512159,0.001458597],"category_scores_gemma":[0.003191988,0.0001228025,0.0008761061,0.004049641,0.0001961453,0.0007255476,0.0004572021,0.000406845,0.001123558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003962175,"about_ca_system_score_gemma":0.0007361552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006021698,"about_ca_topic_score_gemma":0.0009422931,"domain_scores_codex":[0.9993375,0.0001160698,0.0001193072,0.0001508728,0.0002172907,0.00005905606],"domain_scores_gemma":[0.9970176,0.001549348,0.0007397699,0.0001166806,0.0004380568,0.0001385503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002037764,0.0007967841,0.3610033,0.005559856,0.0009133429,0.001430807,0.0001787492,0.02126725,0.1839817,0.001028849,0.009605977,0.4121955],"study_design_scores_gemma":[0.0001921489,0.002251938,0.663892,0.0009478517,0.001987723,0.003351036,0.000840985,0.1622104,0.1140922,0.008668185,0.04140919,0.0001563216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8614238,0.01801295,0.05022315,0.001420587,0.0001624416,0.0005118765,0.05909815,0.001727021,0.007419993],"genre_scores_gemma":[0.898002,0.003958629,0.05043491,0.0002979359,0.000258883,0.0004226385,0.04540398,0.0000735489,0.001147474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006707818,"threshold_uncertainty_score":0.005850077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05382378434079933,"score_gpt":0.4168866432617274,"score_spread":0.363062858920928,"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."}}