{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001946856,0.0001427914,0.0001648987,0.00002099619,0.00005870812,0.00003984497,0.0001454293,0.0001748546,0.00003236419],"category_scores_gemma":[0.0002926723,0.0001233442,0.00005545649,0.0000981863,0.0000198142,0.000005300334,0.00003734177,0.00009139204,0.00001697735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001273379,"about_ca_system_score_gemma":0.00004697779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003262982,"about_ca_topic_score_gemma":0.00001264964,"domain_scores_codex":[0.9991742,0.00008365151,0.0002802198,0.0002083644,0.00009072459,0.0001628781],"domain_scores_gemma":[0.9994394,0.00001288799,0.0001126074,0.0002027495,0.0001145569,0.0001178436],"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":[0.001160048,0.00003269859,0.002687766,0.0003234492,0.00008238306,8.637679e-7,0.0004610982,0.0008062216,0.964164,0.00003404706,0.02785678,0.002390601],"study_design_scores_gemma":[0.002044416,0.002197546,0.0003133489,0.0000383498,0.0000421488,0.00001152566,0.0001593703,0.2455145,0.05833066,0.000001584204,0.6910709,0.0002756095],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.937378,0.00006103004,0.05928955,0.0001202947,0.001423108,0.0008209337,0.0001761739,0.0001324085,0.0005984387],"genre_scores_gemma":[0.9931807,0.000004605562,0.0008367431,0.0001972544,0.001388392,0.00002959533,0.004011312,0.00002958204,0.0003218018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9058334,"threshold_uncertainty_score":0.502983,"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."}}