{"id":"W2131675589","doi":"10.1093/bioinformatics/btt769","title":"SNPdryad: predicting deleterious non-synonymous human SNPs using only orthologous protein sequences","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Classifier (UML); Biology; Computational biology; Protein sequencing; Genome; Sequence alignment; Human proteome project; Proteome; Computer science; Artificial intelligence; Machine learning; Genetics; Gene; Peptide sequence; Proteomics","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":[0.000287507,0.0002377583,0.0002277065,0.00005380058,0.0003253529,0.00006299873,0.0002642449,0.0001663495,0.000004722836],"category_scores_gemma":[0.00007159198,0.0002156596,0.00009580039,0.00007406644,0.0001372411,0.000003354885,0.0002235823,0.00009777059,0.00001177546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001961259,"about_ca_system_score_gemma":0.0000867446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005981109,"about_ca_topic_score_gemma":0.00006604606,"domain_scores_codex":[0.9987563,0.00003421698,0.0004561364,0.0002164937,0.000152679,0.0003841915],"domain_scores_gemma":[0.9991783,0.000009325092,0.0002431411,0.0003853905,0.00009417506,0.00008960406],"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":[0.00002507903,0.00005245999,0.0164928,0.0001796406,0.0001340978,0.000004412805,0.0008363873,0.0007436132,0.9672883,0.0001662953,0.000236803,0.01384014],"study_design_scores_gemma":[0.006710344,0.009005519,0.04990917,0.0006198909,0.0005097471,0.001314275,0.004827891,0.1119645,0.7098463,0.001750207,0.09796305,0.00557908],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906318,0.0001431646,0.005175341,0.00002306925,0.000149941,0.0003444066,0.00002336119,0.00001517138,0.003493726],"genre_scores_gemma":[0.975359,0.00002611018,0.02387984,0.0002017515,0.0002766273,0.00001796387,0.00003914553,0.00002535804,0.0001742625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.257442,"threshold_uncertainty_score":0.8794345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01526174768194568,"score_gpt":0.2468313427302725,"score_spread":0.2315695950483268,"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."}}