{"id":"W2117428399","doi":"10.1093/bioinformatics/btu665","title":"ExomeAI: detection of recurrent allelic imbalance in tumors using whole-exome sequencing data","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University and Génome Québec Innovation Centre","funders":"Canadian Institutes of Health Research; Diamond Blackfan Anemia Foundation","keywords":"Exome sequencing; Allele; Exome; Genetics; Computational biology; Computer science; Biology; Gene; Mutation","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.005707534,0.001141861,0.001147836,0.004909229,0.0008472202,0.002311894,0.00153963,0.0009572657,0.02707791],"category_scores_gemma":[0.01683193,0.0006739015,0.00123709,0.003924171,0.0004450729,0.0009293818,0.00281403,0.001726053,0.007476798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006993468,"about_ca_system_score_gemma":0.001769429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00298384,"about_ca_topic_score_gemma":0.008201532,"domain_scores_codex":[0.9974825,0.0003993047,0.0003080207,0.000996936,0.0006473417,0.0001659987],"domain_scores_gemma":[0.9928883,0.003815233,0.001037837,0.00101984,0.0007242166,0.0005146403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002305951,0.0002264935,0.1281077,0.003586361,0.002441835,0.003799834,0.0009533886,0.009244938,0.09018844,0.007598957,0.4846422,0.2669038],"study_design_scores_gemma":[0.001227274,0.0005342675,0.3514375,0.0007529394,0.001387916,0.008256793,0.0005004535,0.0836217,0.119519,0.03105513,0.4011809,0.0005260897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.1114118,0.001494397,0.3296297,0.002458556,0.0004466728,0.001288973,0.4748378,0.0644711,0.01396105],"genre_scores_gemma":[0.2150565,0.001075737,0.3903717,0.001371131,0.0002881866,0.002789695,0.3664317,0.01154813,0.0110672],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02707791,"threshold_uncertainty_score":0.09058464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03263808090979239,"score_gpt":0.2651874694931112,"score_spread":0.2325493885833188,"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."}}