{"id":"W4387491258","doi":"10.21203/rs.3.rs-3405211/v1","title":"Predicting molecular events underlying rare diseases using variant annotation, aberrant gene expression events, and human phenotype ontology","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Hospital for Sick Children; Deutsche Forschungsgemeinschaft; Bundesministerium für Bildung und Forschung; Genome Canada","keywords":"Transcriptome; Computational biology; Biology; Disease; Gene; Omics; OMIM : Online Mendelian Inheritance in Man; DNA sequencing; Mendelian inheritance; Phenotype; RNA-Seq; Genome; Genetics; Bioinformatics; Gene expression; Medicine; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001091619,0.0008321707,0.000527503,0.003259996,0.000439551,0.001463728,0.0006740264,0.0008013135,0.003170569],"category_scores_gemma":[0.002835861,0.0003179368,0.001234943,0.002400312,0.0003614901,0.00107075,0.0009150321,0.000726929,0.0007244021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005275933,"about_ca_system_score_gemma":0.001077466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004159531,"about_ca_topic_score_gemma":0.005694057,"domain_scores_codex":[0.9994348,0.00008455277,0.00005382031,0.0001986995,0.0001534525,0.00007463095],"domain_scores_gemma":[0.998632,0.0007544635,0.0001909908,0.0002328852,0.0001011485,0.00008852409],"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":[0.004082256,0.0005499505,0.2909999,0.002478185,0.0015895,0.01199019,0.0009149035,0.07177883,0.1360144,0.06415573,0.05095837,0.3644878],"study_design_scores_gemma":[0.0005508219,0.0002908147,0.1681229,0.0003263157,0.001581282,0.008003718,0.0006973175,0.4585473,0.06236753,0.2300585,0.06929387,0.0001595098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4374497,0.002812234,0.4398506,0.001617601,0.0004012311,0.0001939296,0.09587754,0.01360472,0.008192373],"genre_scores_gemma":[0.7332774,0.002279737,0.1845844,0.0002130604,0.0001848633,0.0001059783,0.07603423,0.001009401,0.002310934],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004159531,"threshold_uncertainty_score":0.01060665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08164533114627637,"score_gpt":0.3935829610585886,"score_spread":0.3119376299123123,"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."}}