{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005957267,0.0001086689,0.0002547256,0.00006547867,0.0000124049,0.000009873895,0.0003375719,0.0002028987,0.00003716299],"category_scores_gemma":[0.000306196,0.00008711377,0.00007939614,0.000108113,0.0002497431,0.000008326595,0.00006041297,0.0002621986,0.000001969288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001060229,"about_ca_system_score_gemma":0.00004081899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001734507,"about_ca_topic_score_gemma":6.816172e-7,"domain_scores_codex":[0.9986539,0.000280921,0.0007094975,0.0001402845,0.0000462562,0.0001691477],"domain_scores_gemma":[0.9993254,0.00002170278,0.0003528283,0.0001468844,0.00006932677,0.00008387052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004605156,0.0001630791,0.006698459,0.00002780651,0.00002848699,6.304909e-7,0.0002181893,0.0007272408,0.976724,0.01290096,0.0000673095,0.001983272],"study_design_scores_gemma":[0.009228059,0.01351742,0.03623743,0.00006611375,0.000190147,0.0004184694,0.002137864,0.1920961,0.6905261,0.04524589,0.00894907,0.001387357],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9569008,0.000763802,0.04101153,0.0007718689,0.0001120721,0.0001109613,0.00001329276,0.00000286088,0.0003128554],"genre_scores_gemma":[0.9982897,0.0000939249,0.001087587,0.0003538916,0.00009758223,0.000001608764,0.00006247075,0.00001002479,0.000003200908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.286198,"threshold_uncertainty_score":0.3552396,"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."}}