{"id":"W4210337976","doi":"10.1038/s41540-022-00212-1","title":"High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds","year":2022,"lang":"en","type":"article","venue":"npj Systems Biology and Applications","topic":"Fungal and yeast genetics research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"National Human Genome Research Institute; Canadian Institutes of Health Research; Great Lakes Bioenergy Research Center; Japan Society for the Promotion of Science; Office of Science; Ministry of Education, Culture, Sports, Science and Technology; University of Tokyo; Government of Canada; U.S. Department of Energy","keywords":"Biology; Computational biology; Profiling (computer programming); Yeast; Antifungal drug; Antifungal; Chemistry; Biochemistry; Computer science; Microbiology","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":[0.0002796854,0.00008683564,0.0001324645,0.00002449458,0.0003861999,0.000007803504,0.0002132155,0.00009633986,0.000005792693],"category_scores_gemma":[0.00001805616,0.00006060902,0.00004398571,0.0000846214,0.0002433923,0.000001496645,0.0002162359,0.0001108397,0.000001398509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001012651,"about_ca_system_score_gemma":0.00004786707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000036087,"about_ca_topic_score_gemma":0.000003288155,"domain_scores_codex":[0.9992388,0.00007020913,0.0001810463,0.0002691088,0.00006748112,0.0001733393],"domain_scores_gemma":[0.9995137,0.00005148881,0.0000902695,0.0002241257,0.00008210231,0.00003830996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001685862,0.0001124006,0.00278162,0.00004480475,0.00009243727,2.40776e-7,0.00003735356,0.0003425039,0.9623611,0.03239514,0.001014523,0.0006493282],"study_design_scores_gemma":[0.002559008,0.004239018,0.004647403,0.00001479537,0.0000949861,0.0002426871,0.002887198,0.002505953,0.3491776,0.007028018,0.6260048,0.0005985455],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602828,0.004855128,0.0282889,0.0005212339,0.0003147538,0.002768565,0.002057336,0.00002146076,0.0008898147],"genre_scores_gemma":[0.996126,0.0000780436,0.0004575414,0.00004773684,0.0002219364,0.002185269,0.0006474982,0.000008249525,0.0002277277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6249902,"threshold_uncertainty_score":0.2970376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02397503302940388,"score_gpt":0.2953436316948617,"score_spread":0.2713685986654578,"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."}}