{"id":"W2150408357","doi":"10.1093/nar/gkh485","title":"Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations","year":2004,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Proteome; Gene ontology; Classifier (UML); Naive Bayes classifier; Computer science; Biology; Human proteome project; Bayes' theorem; Function (biology); Machine learning; Protein function; Artificial intelligence; Protein function prediction; Computational biology; Bioinformatics; Gene; Proteomics; Bayesian probability; Genetics; Support vector machine; Gene expression","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002689346,0.002625271,0.000723227,0.002261294,0.0005909337,0.001550094,0.002884675,0.002117124,0.04132002],"category_scores_gemma":[0.009795385,0.001436249,0.001567198,0.001357093,0.0004914353,0.003302589,0.002133062,0.002030793,0.01353179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008527251,"about_ca_system_score_gemma":0.001285451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002311546,"about_ca_topic_score_gemma":0.003356237,"domain_scores_codex":[0.9991112,0.0001776011,0.0001028947,0.0001945027,0.0003540201,0.00005971053],"domain_scores_gemma":[0.9927657,0.005249213,0.0004741054,0.0007959855,0.000538181,0.0001768848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002223543,0.0005912882,0.009890618,0.002475045,0.0003313418,0.002561414,0.001082284,0.02756136,0.02689051,0.02167264,0.508059,0.396661],"study_design_scores_gemma":[0.000940495,0.0001914116,0.005896262,0.0004758934,0.0002114997,0.001596675,0.0003026685,0.5937495,0.07465773,0.05536639,0.2661898,0.0004217155],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.004116299,0.0001147863,0.4577691,0.0004210507,0.00008909657,0.0002504849,0.01471376,0.5206503,0.001875192],"genre_scores_gemma":[0.07203792,0.0005518846,0.830804,0.0008915685,0.0001317633,0.001220897,0.05303964,0.0323947,0.008927695],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.04132002,"threshold_uncertainty_score":0.1382293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02065510645228343,"score_gpt":0.3270776948286184,"score_spread":0.306422588376335,"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."}}