{"id":"W3047430701","doi":"10.1128/msystems.00288-20","title":"T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors","year":2020,"lang":"en","type":"article","venue":"mSystems","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Shenzhen Peacock Plan; Shenzhen Municipal Human Resources and Social Security Bureau","keywords":"Effector; Computational biology; Pipeline (software); Computer science; Machine learning; Artificial intelligence; Biology; Software; False positive rate; Signal peptide; Bioinformatics; Peptide sequence; Gene; Genetics; Cell biology; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00102424,0.002322204,0.001022378,0.001173463,0.0006877647,0.001183341,0.001380421,0.0009195257,0.004015105],"category_scores_gemma":[0.002156013,0.0007147721,0.002019227,0.0009864842,0.0003177892,0.001447807,0.001517084,0.001625362,0.002813749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000560923,"about_ca_system_score_gemma":0.001651482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003446271,"about_ca_topic_score_gemma":0.003540395,"domain_scores_codex":[0.9995591,0.00006662868,0.00003424541,0.0001472129,0.0001336961,0.00005918475],"domain_scores_gemma":[0.9995953,0.0001415999,0.00003929904,0.00006287843,0.0001088891,0.00005199906],"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":[0.003472339,0.0009219366,0.0240236,0.002693928,0.001517644,0.00228542,0.0005558979,0.1865973,0.1957944,0.009925819,0.1580721,0.4141397],"study_design_scores_gemma":[0.0001404491,0.0002352016,0.00346425,0.00005160184,0.0001438267,0.0003256069,0.00007561846,0.9203553,0.04613421,0.006837888,0.02212202,0.0001139263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.0699185,0.001005981,0.7059408,0.0005948311,0.0002309158,0.0002976254,0.02018017,0.1995176,0.002313558],"genre_scores_gemma":[0.3471386,0.001294167,0.5588791,0.0004765139,0.00009603023,0.0005715928,0.08041694,0.008115451,0.003011604],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.004015105,"threshold_uncertainty_score":0.01343191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01235728440646873,"score_gpt":0.2518619254134165,"score_spread":0.2395046410069478,"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."}}