{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002262119,0.0001013878,0.0001310907,0.00006261723,0.00003338725,0.00001536032,0.0002623101,0.00006760229,0.00000152864],"category_scores_gemma":[0.0001068281,0.00009625147,0.00003492737,0.00008657506,0.00003615486,0.00001227157,0.0001928674,0.00005104405,0.000004179728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002675363,"about_ca_system_score_gemma":0.00008079903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002792003,"about_ca_topic_score_gemma":0.00005185553,"domain_scores_codex":[0.9992426,0.00002110585,0.0003427869,0.0001344685,0.00009457451,0.0001644481],"domain_scores_gemma":[0.9992291,0.00000669367,0.0001664438,0.000504342,0.00004386715,0.00004951792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008816553,0.00009490112,0.005298081,0.0004973975,0.00004611017,0.000003183331,0.0004085926,0.002889729,0.9628163,0.00005236973,0.0001901024,0.02761505],"study_design_scores_gemma":[0.001814787,0.0006067551,0.01926946,0.0003438192,0.00006079373,0.0001072666,0.001443458,0.811106,0.1382735,0.0001697898,0.02593833,0.0008660008],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898387,0.0002806147,0.009358167,0.000009491836,0.0001211835,0.0001091066,0.00007082577,0.000005748449,0.0002062201],"genre_scores_gemma":[0.9956844,0.00007772358,0.003827093,0.00005553969,0.00006903094,0.000002105145,0.000263206,0.000009376158,0.00001150289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8245428,"threshold_uncertainty_score":0.3925021,"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."}}