{"id":"W4391104625","doi":"10.1038/s41467-024-44980-2","title":"Pangenome graphs improve the analysis of structural variants in rare genetic diseases","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill Genome Centre; McGill University Health Centre; McGill University","funders":"Children's Mercy Hospital","keywords":"Computational biology; Genetics; Biology; Computer science; Evolutionary biology","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":[0.00005169021,0.00008674539,0.0001130908,0.0001384366,0.00008097944,0.00003053047,0.0006897728,0.0001153488,0.00001299842],"category_scores_gemma":[0.00005026005,0.00006124231,0.0001791511,0.0005922503,0.0001085913,0.000002682877,0.0002503802,0.0001767999,9.795439e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009454804,"about_ca_system_score_gemma":0.00007510153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004303382,"about_ca_topic_score_gemma":0.000782117,"domain_scores_codex":[0.9994148,0.00007617884,0.0001636753,0.0001743592,0.00006750839,0.0001034964],"domain_scores_gemma":[0.9986251,0.00004988086,0.00004032034,0.001188247,0.00006073176,0.00003574329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001983158,0.0006726815,0.4676841,0.0002593744,0.0114984,0.00004909532,0.001882998,0.003247842,0.4219777,0.04124542,0.004903665,0.04638042],"study_design_scores_gemma":[0.00008787517,0.0000251955,0.9883347,0.00001048638,0.0007424686,0.000003112033,0.0001317544,0.002522439,0.0004464871,0.0008460014,0.006716819,0.0001326325],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8626616,0.1355818,0.00001476974,0.0005144424,0.0001136077,0.0001503424,0.0007902957,0.000008315814,0.0001648006],"genre_scores_gemma":[0.9969375,0.001834554,0.0002604556,0.000119544,0.0000271738,0.00002758552,0.0007438381,0.00001005341,0.0000392754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5206507,"threshold_uncertainty_score":0.2497389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006567197177995857,"score_gpt":0.2745282427646974,"score_spread":0.2679610455867016,"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."}}