{"id":"W3213337307","doi":"10.1038/s41467-021-26850-3","title":"Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Antifungal resistance and susceptibility","field":"Medicine","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"Japan Society for the Promotion of Science; National Institutes of Health; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; National Institute of Allergy and Infectious Diseases; Japan Agency for Medical Research and Development; Canadian Institute for Advanced Research; Foundation for the National Institutes of Health","keywords":"Candida albicans; Computational biology; Antifungal; Genome; Function (biology); Biology; Gene; Fungal pathogen; Transfer RNA; Functional genomics; Genomics; Genetics; Microbiology; RNA","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005000939,0.000579668,0.0005213775,0.0006692599,0.0002367332,0.0004742558,0.000312524,0.0002917189,0.0007191385],"category_scores_gemma":[0.00113673,0.0001468749,0.0005443494,0.0005043357,0.0001954351,0.0004596565,0.0003136138,0.0004989504,0.0002274487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003907454,"about_ca_system_score_gemma":0.0004217357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001700535,"about_ca_topic_score_gemma":0.003241257,"domain_scores_codex":[0.9997903,0.00005363988,0.000009535965,0.00007874603,0.00004498137,0.00002289785],"domain_scores_gemma":[0.9995064,0.0003195142,0.00006018628,0.00004053731,0.00004689852,0.00002652097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001237309,0.0006598696,0.2031381,0.0003918568,0.0004795972,0.0005553475,0.00009006554,0.3630391,0.3064684,0.002505288,0.00212292,0.1193122],"study_design_scores_gemma":[0.00003154091,0.0002324587,0.02605993,0.00001291969,0.000113184,0.0001219921,0.00005385077,0.9361331,0.03030473,0.00471783,0.002194394,0.00002415887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9330885,0.0009694713,0.05909905,0.0004549873,0.00002375073,0.00004627155,0.003487525,0.0007296827,0.002100647],"genre_scores_gemma":[0.9690949,0.0003996314,0.02473669,0.0001427382,0.00001597888,0.00003914025,0.005223157,0.00005281937,0.0002949396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001700535,"threshold_uncertainty_score":0.003381252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02949897689431573,"score_gpt":0.3606533174742518,"score_spread":0.3311543405799361,"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."}}