{"id":"W1982075805","doi":"10.1109/cibcb.2005.1594924","title":"Pathway Analyst Automated Metabolic Pathway Prediction","year":2005,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Leverage (statistics); Metabolic pathway; Hidden Markov model; Computer science; Support vector machine; Similarity (geometry); Computational biology; Identification (biology); Artificial intelligence; Machine learning; Biology; Gene; Genetics; Ecology","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":[0.0002116038,0.0001491462,0.0001307597,0.00006205493,0.00007999344,0.00003289367,0.0001613296,0.0001173512,0.0001703687],"category_scores_gemma":[0.00009364801,0.000125176,0.00008841731,0.0001257201,0.00003365121,0.00000723491,0.00008034776,0.000100813,0.0001619932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001005859,"about_ca_system_score_gemma":0.00005948549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001150043,"about_ca_topic_score_gemma":0.00003293667,"domain_scores_codex":[0.9990924,0.00004750037,0.0002884414,0.0001969003,0.0001528865,0.0002218466],"domain_scores_gemma":[0.9993833,0.000006092173,0.00009236404,0.0003654077,0.00006833224,0.00008455366],"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":[0.00007740264,0.0002898914,0.02523082,0.00005657539,0.0004311809,0.000003384981,0.000335437,0.01615652,0.8212398,0.003484428,0.03879217,0.09390236],"study_design_scores_gemma":[0.000453512,0.0001350038,0.009977645,0.000004447444,0.00002060537,0.0000253625,0.00003869825,0.1248124,0.1943791,0.000006929264,0.6699371,0.0002092555],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8988413,0.0005796233,0.01232029,0.0003958716,0.000262111,0.0002816568,0.00007100355,0.0007577681,0.08649035],"genre_scores_gemma":[0.9839563,0.00005494549,0.01024845,0.0004964104,0.0004061963,0.0000130496,0.0003919556,0.00002059336,0.004412054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6311449,"threshold_uncertainty_score":0.510453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005055874996643707,"score_gpt":0.2352774308702761,"score_spread":0.2302215558736324,"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."}}