{"id":"W2104746990","doi":"10.2174/138620708785739934","title":"Design of Phenotypic Screens for Bioactive Chemicals and Identification of their Targets by Genetic and Proteomic Approaches","year":2008,"lang":"en","type":"review","venue":"Combinatorial Chemistry & High Throughput Screening","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Identification (biology); Computational biology; Phenotypic screening; Proteome; Phenotype; Computer science; Biology; Bioinformatics; Genetics; Gene","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":[0.001093135,0.001244975,0.00161794,0.002745979,0.0002393938,0.0008727417,0.00113307,0.0008000862,0.001382497],"category_scores_gemma":[0.0005966267,0.0004788219,0.0006337726,0.00164228,0.0008290858,0.001060406,0.0004849848,0.001223501,0.001595418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007560134,"about_ca_system_score_gemma":0.000655522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003837667,"about_ca_topic_score_gemma":0.0007010871,"domain_scores_codex":[0.9995523,0.00007717931,0.00003860304,0.00007729731,0.0002224379,0.0000321214],"domain_scores_gemma":[0.999706,0.0001297214,0.0000450436,0.00002330713,0.0000716685,0.00002431114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001290396,0.0003433637,0.0006511879,0.008890134,0.0001021132,0.0005133748,0.00005349429,0.002362636,0.3437003,0.008926316,0.003298968,0.6310291],"study_design_scores_gemma":[0.0002289383,0.001816371,0.004728468,0.001088118,0.0003407783,0.005370843,0.0001400681,0.003419848,0.43287,0.006493981,0.5433791,0.0001234819],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.02404965,0.8260025,0.123596,0.001137264,0.0005297263,0.001095825,0.0005806979,0.0007575657,0.02225081],"genre_scores_gemma":[0.04614154,0.8362723,0.1072593,0.0007576981,0.0001906677,0.0007858407,0.0006904439,0.00004834418,0.007853915],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002745979,"threshold_uncertainty_score":0.005781114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05448709672688282,"score_gpt":0.2726467984811234,"score_spread":0.2181597017542406,"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."}}