{"id":"W91916607","doi":"10.1007/978-1-4939-2269-7_23","title":"Chemical Genomic Profiling via Barcode Sequencing to Predict Compound Mode of Action","year":2014,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Occupational Cancer Research Centre; University of Toronto","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute; Canadian Institutes of Health Research","keywords":"Barcode; Profiling (computer programming); Computational biology; Mode of action; Genomics; Saccharomyces cerevisiae; DNA sequencing; Computer science; Biology; Genetics; DNA; Gene; Genome; Biochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0007651947,0.000124932,0.0003887856,0.0001456115,0.00001659391,0.000002930409,0.0001089983,0.0001847428,0.00001630222],"category_scores_gemma":[0.0007874672,0.0001008193,0.00007143948,0.0001729883,0.00007603732,0.0000119713,0.00007768801,0.0002239922,0.000002942229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001108637,"about_ca_system_score_gemma":0.00004181625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008833365,"about_ca_topic_score_gemma":0.000002987962,"domain_scores_codex":[0.9985909,0.0004697653,0.0003159323,0.0003441112,0.00005395569,0.000225325],"domain_scores_gemma":[0.9994191,0.0001169016,0.00007427608,0.0002644856,0.0000583043,0.00006694216],"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":[0.0001158903,0.00002281914,0.0003677232,0.0000679556,0.00002374053,0.000003347949,0.00003760887,0.00006075514,0.9739866,0.0002442668,0.000006266811,0.02506308],"study_design_scores_gemma":[0.0002784965,0.0001321707,0.00008932847,0.00004030604,0.00003255173,0.00004254819,0.00001113222,0.003401733,0.9942027,0.00113701,0.0005334296,0.00009858277],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5398893,0.00006007594,0.4593185,0.0002777481,0.00009007133,0.000213567,0.000002691363,0.00001436603,0.0001336527],"genre_scores_gemma":[0.4873182,0.000004362098,0.5123074,0.0002777053,0.00006146412,0.000001715073,0.00001038807,0.000009400412,0.000009336559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05298889,"threshold_uncertainty_score":0.4111293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03406619873020545,"score_gpt":0.3828063933750657,"score_spread":0.3487401946448602,"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."}}