{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000331242,0.0005549567,0.002159209,0.00005787386,0.0001206527,0.00002143565,0.0002255798,0.000639148,0.000008007752],"category_scores_gemma":[0.0003652234,0.0004357267,0.0002553086,0.0002265967,0.0004835351,0.00007859282,0.0001392428,0.0003506072,2.730155e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006606343,"about_ca_system_score_gemma":0.000163077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002184209,"about_ca_topic_score_gemma":2.455628e-8,"domain_scores_codex":[0.9975058,0.00008924738,0.00104528,0.0008298392,0.0002258441,0.0003039849],"domain_scores_gemma":[0.9977013,0.0003238975,0.001156918,0.0004646544,0.0002351177,0.0001180715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005446,0.0002788607,0.000003449675,0.03628363,0.0009860874,0.000002012818,0.0001273581,8.1775e-7,0.7358195,0.00005234213,0.0009023253,0.224999],"study_design_scores_gemma":[0.00147549,0.0001667894,0.000009075941,0.004601661,0.001342428,0.00008406657,0.00003098796,0.00003770014,0.97271,0.0002930976,0.01873955,0.0005091515],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.005068681,0.9881234,0.003179616,0.00005281372,0.000141399,0.003033069,0.0003553732,0.00003366239,0.00001202476],"genre_scores_gemma":[0.04862214,0.9396892,0.01047192,0.000004486234,0.0005131102,0.00005861293,0.0004720625,0.00009076724,0.00007768984],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.2368905,"threshold_uncertainty_score":0.9998094,"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."}}