{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00202924,0.001363024,0.001370824,0.001874738,0.0007741307,0.0009205745,0.001977136,0.001236557,0.00296046],"category_scores_gemma":[0.003294437,0.0006593035,0.0008577045,0.001574802,0.0004628439,0.001035125,0.001251471,0.001141375,0.002094776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005684217,"about_ca_system_score_gemma":0.001357271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003596105,"about_ca_topic_score_gemma":0.007063418,"domain_scores_codex":[0.9989318,0.0002256716,0.00007718365,0.0003866877,0.0002920315,0.00008668379],"domain_scores_gemma":[0.998618,0.0007082486,0.0001690076,0.0001411603,0.0002302601,0.0001333156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004974386,0.000825747,0.1610833,0.002191443,0.0009722335,0.001464239,0.0004256723,0.2180289,0.06041318,0.005364705,0.1334257,0.4108306],"study_design_scores_gemma":[0.0005347473,0.0004454193,0.01681077,0.0001150386,0.0001728325,0.001332892,0.0001241075,0.9235489,0.03094081,0.006891756,0.01895324,0.0001294732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4847347,0.003842676,0.4059854,0.00106934,0.0006168363,0.000558085,0.03406218,0.06381778,0.005312907],"genre_scores_gemma":[0.5011107,0.0008495786,0.4493087,0.0005621185,0.0001674605,0.0003058305,0.04405542,0.001546738,0.002093439],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003596105,"threshold_uncertainty_score":0.01073176,"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."}}