{"id":"W4386583626","doi":"10.1101/2023.09.08.556885","title":"Machine learning to identify clinically relevant <i>Candida</i> yeast species","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Precision Laboratories; Alberta Machine Intelligence Institute; University of Alberta; Government of Alberta","keywords":"Budding yeast; Candida albicans; Candida glabrata; Yeast; Artificial intelligence; Corpus albicans; Convolutional neural network; Candida auris; Biology; Antifungal; Microbiology; Saccharomyces cerevisiae; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000622165,0.0008339754,0.0003335171,0.0008566456,0.0001709005,0.0006530421,0.0004561226,0.0005781595,0.001074174],"category_scores_gemma":[0.001227976,0.0001728108,0.0005084777,0.0003503998,0.0001702517,0.0004041916,0.0003199577,0.0003872948,0.0003942231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006932536,"about_ca_system_score_gemma":0.0005177606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006299136,"about_ca_topic_score_gemma":0.00716936,"domain_scores_codex":[0.9997867,0.00003504557,0.00001648139,0.00005857007,0.00005320014,0.00005012899],"domain_scores_gemma":[0.9995292,0.000141904,0.00008612272,0.00003448766,0.000173432,0.00003476754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001318076,0.0006341817,0.1738724,0.0005560046,0.0004023757,0.0006259779,0.00008483396,0.1803664,0.1019671,0.0008613265,0.007723141,0.5315883],"study_design_scores_gemma":[0.00001905476,0.0002548774,0.02519109,0.00006557289,0.0001083881,0.0003968118,0.00004905098,0.9361943,0.03560465,0.0006068103,0.00148852,0.00002076169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9301082,0.00368654,0.05788334,0.000537606,0.0001380399,0.0000844887,0.001094239,0.001459413,0.005008082],"genre_scores_gemma":[0.9766691,0.0005350227,0.02033043,0.0001530029,0.00002251969,0.00001923749,0.00105075,0.00002509633,0.001194905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006299136,"threshold_uncertainty_score":0.01252496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02873975925884997,"score_gpt":0.2716680520894403,"score_spread":0.2429282928305903,"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."}}