{"id":"W2029503266","doi":"10.1110/ps.9.12.2344","title":"Functional prediction: Identification of protein orthologs and paralogs","year":2000,"lang":"en","type":"article","venue":"Protein Science","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Medical Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Computational biology; Biology; Identification (biology); Gene; Genetics; Function (biology); Protein function; Similarity (geometry); Protein family; Evolutionary biology; Computer science; Ecology","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":[0.00119654,0.0006022258,0.0006589957,0.001611993,0.0005907161,0.0007384419,0.0006766849,0.0008026421,0.002512529],"category_scores_gemma":[0.002544173,0.0002539321,0.0004830311,0.0009134088,0.0004746251,0.001374015,0.0005077181,0.0009848028,0.002170923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002529378,"about_ca_system_score_gemma":0.0005296943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000535042,"about_ca_topic_score_gemma":0.0003761937,"domain_scores_codex":[0.9995242,0.0001331537,0.00004346447,0.0001505069,0.00009540818,0.0000531595],"domain_scores_gemma":[0.9986663,0.0005139479,0.0001522177,0.000191366,0.0002859046,0.0001901345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001736823,0.000537834,0.05302731,0.0009344795,0.00008956687,0.00215721,0.0004215308,0.004163875,0.6980248,0.005871094,0.004563587,0.2284719],"study_design_scores_gemma":[0.0004653061,0.00169883,0.1805809,0.0002155946,0.0004298193,0.0172125,0.001825521,0.2896467,0.4077929,0.04783408,0.05207757,0.0002203257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6121789,0.002802671,0.3722795,0.0012465,0.0001730783,0.0001811245,0.003162827,0.003136493,0.004838941],"genre_scores_gemma":[0.8585569,0.0008952989,0.1307192,0.0002227413,0.000114122,0.0001005373,0.007637874,0.0002547308,0.001498617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002512529,"threshold_uncertainty_score":0.008405209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006374154517609946,"score_gpt":0.2083852407109648,"score_spread":0.2020110861933549,"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."}}