{"id":"W1547580714","doi":"10.1002/ddr.21035","title":"Harnessing Omics Sciences, Population Databases, and Open Innovation Models for Theranostics‐Guided Drug Discovery and Development","year":2012,"lang":"en","type":"article","venue":"Drug Development Research","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Drug discovery; Omics; Drug; Population; Medicine; Pharmacology; Data science; Computer science; Computational biology; Bioinformatics; Biology","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":[0.06992137,0.0008982059,0.001225328,0.006007353,0.002052418,0.01943588,0.003221063,0.003486294,0.005488377],"category_scores_gemma":[0.0519834,0.000636294,0.001663757,0.004199407,0.01350527,0.01856774,0.00966655,0.004442882,0.00114464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006308127,"about_ca_system_score_gemma":0.01031103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001778639,"about_ca_topic_score_gemma":0.001653687,"domain_scores_codex":[0.9654526,0.02412298,0.001718036,0.002353575,0.005578037,0.0007748468],"domain_scores_gemma":[0.8990557,0.07230306,0.007085348,0.01297108,0.006107013,0.002477753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002715912,0.00004999238,0.001066947,0.0001346797,0.00003043467,0.00004098277,0.0002489815,0.003214819,0.0001488892,0.9748742,0.00154174,0.01862126],"study_design_scores_gemma":[0.00002400985,0.00005034277,0.0004399885,0.0002593843,0.00002985061,0.00005863951,0.0003667606,0.01670536,0.0004386685,0.9576606,0.02393501,0.00003134798],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0157082,0.006554704,0.8168839,0.09942836,0.0006782449,0.0005520043,0.0008976129,0.000605233,0.05869169],"genre_scores_gemma":[0.5183336,0.01117809,0.4521281,0.008456253,0.001192621,0.001481312,0.0007199041,0.0001581159,0.006352045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06992137,"threshold_uncertainty_score":0.369784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3186572337192796,"score_gpt":0.4593285908002595,"score_spread":0.1406713570809799,"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."}}