{"id":"W1990464724","doi":"10.1586/epr.12.22","title":"Chemical proteomics and its impact on the drug discovery process","year":2012,"lang":"en","type":"review","venue":"Expert Review of Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Hospital and Health Sciences Centre; University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Drug discovery; Proteomics; Computer science; Computational biology; Proteome; Drug development; Data science; Process (computing); Drug target; Identification (biology); Business process discovery; Drug; Bioinformatics; Chemistry; Biology; Pharmacology; Work in process; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000604092,0.0009603706,0.002420168,0.00006070319,0.0001417842,0.00006211663,0.001154413,0.0004238899,0.0001942291],"category_scores_gemma":[0.0002556712,0.0005406215,0.0009011368,0.0003106919,0.0001748417,0.0002437832,0.0003418328,0.001202677,0.00002648171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002681884,"about_ca_system_score_gemma":0.0003464532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005414489,"about_ca_topic_score_gemma":1.403658e-7,"domain_scores_codex":[0.9969372,0.00008833855,0.001325376,0.0007275484,0.0003825139,0.0005390457],"domain_scores_gemma":[0.9965975,0.000263341,0.001509127,0.001300838,0.0001425283,0.0001866664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006660588,0.0006137607,0.00000188938,0.6398053,0.0004902373,0.000003544233,0.0002461,4.514222e-7,0.009152646,0.009127191,0.00235322,0.3381391],"study_design_scores_gemma":[0.0001527049,0.00003655119,3.580215e-8,0.2339469,0.0006101902,0.0001113883,0.00001814108,0.00001270098,0.08019783,0.000791368,0.682878,0.001244174],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001074484,0.991925,0.0007041317,0.0002561946,0.00003215765,0.005826378,0.0003232744,0.00009561293,0.0007297868],"genre_scores_gemma":[0.00002949578,0.9733037,0.01545371,0.0002009202,0.0004994171,0.009921466,0.0001870363,0.0001721852,0.0002320906],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.6805248,"threshold_uncertainty_score":0.9997045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03257457122426644,"score_gpt":0.392731006024818,"score_spread":0.3601564348005516,"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."}}