{"id":"W3000297642","doi":"10.1016/j.jtbi.2020.110172","title":"Identification of potential therapeutic targets in Neisseria gonorrhoeae by an in-silico approach","year":2020,"lang":"en","type":"article","venue":"Journal of Theoretical Biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Deutscher Akademischer Austauschdienst; University Grants Commission","keywords":"Neisseria gonorrhoeae; In silico; Identification (biology); Computational biology; Biology; Microbiology; Ecology; Genetics","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":[0.0004888491,0.0007199415,0.00108376,0.0005984899,0.0002982933,0.00105672,0.0005427726,0.0007732611,0.002536713],"category_scores_gemma":[0.00090186,0.0003536722,0.001277585,0.0003237808,0.000168063,0.0003036342,0.0003023107,0.0005667112,0.0005626646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004143258,"about_ca_system_score_gemma":0.0007104175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008944405,"about_ca_topic_score_gemma":0.001754627,"domain_scores_codex":[0.999798,0.00008676227,0.00001488742,0.00002602099,0.00005076334,0.00002354612],"domain_scores_gemma":[0.9997308,0.0001838856,0.00002860547,0.00001282706,0.00003043924,0.00001352685],"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":[0.002010882,0.001330707,0.02239672,0.002657229,0.0009488006,0.002270167,0.0001364859,0.4722369,0.4179411,0.008982916,0.002726224,0.06636191],"study_design_scores_gemma":[0.0001937884,0.001424792,0.003308862,0.0001000999,0.001093671,0.001012998,0.0001240459,0.8484322,0.1306851,0.003458954,0.01011972,0.00004588267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7684944,0.006605656,0.1976629,0.001723667,0.0002246506,0.0006422286,0.005438325,0.002467192,0.01674103],"genre_scores_gemma":[0.9271813,0.002562317,0.06528691,0.0002746191,0.00002640841,0.0001549868,0.002988947,0.00005895894,0.001465485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002536713,"threshold_uncertainty_score":0.008486152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005773743987130411,"score_gpt":0.26106425754155,"score_spread":0.2552905135544196,"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."}}