{"id":"W2118621341","doi":"10.1002/humu.20484","title":"PhenCode: connecting ENCODE data with mutations and phenotype","year":2007,"lang":"en","type":"article","venue":"Human Mutation","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; Montreal Children's Hospital; Hospital for Sick Children","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Ontario Genomics Institute; Cystic Fibrosis Canada; Genome Canada; National Institutes of Health; National Human Genome Research Institute; Cystic Fibrosis Foundation","keywords":"Biology; ENCODE; Genetics; Phenotype; Genome; 1000 Genomes Project; Gene; Computational biology; Single-nucleotide polymorphism; Genotype","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":[0.003558008,0.002166395,0.001274103,0.005475851,0.001016558,0.003029268,0.002231923,0.001913846,0.04784594],"category_scores_gemma":[0.01527244,0.001579914,0.001517796,0.007818297,0.0005771817,0.002601624,0.004383962,0.002000681,0.02304909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001915873,"about_ca_system_score_gemma":0.002704315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01182906,"about_ca_topic_score_gemma":0.01233343,"domain_scores_codex":[0.9975308,0.0005323779,0.0004390989,0.0006875377,0.0006083502,0.0002018152],"domain_scores_gemma":[0.9935133,0.003060776,0.000832242,0.001290192,0.000854452,0.0004491803],"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":[0.002142291,0.0001331448,0.01140905,0.003292122,0.0003743783,0.00118607,0.0006699012,0.004002304,0.005604041,0.01479055,0.9117189,0.04467721],"study_design_scores_gemma":[0.0008653937,0.0001395347,0.01166505,0.0006496488,0.0002309972,0.000772992,0.000331808,0.01406945,0.01234838,0.0263276,0.9323135,0.0002857292],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.004170989,0.0006061048,0.04353134,0.00112399,0.0004491899,0.000260019,0.8189966,0.1209312,0.00993052],"genre_scores_gemma":[0.02433337,0.001082711,0.04902208,0.0009802354,0.0001042579,0.000738818,0.8989093,0.02222922,0.002599985],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.04784594,"threshold_uncertainty_score":0.1600606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247637141668032,"score_gpt":0.2964828771600154,"score_spread":0.2740065057433351,"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."}}