{"id":"W2898168674","doi":"10.1109/access.2018.2877253","title":"Integrated Modeling of GC-Content, Mappability, Tumor Impurity and Aneuploidy for Accurate Detection of Genomic Aberrations","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"BC Cancer Agency; Natural Science Foundation of Ningxia Province; National Natural Science Foundation of China","keywords":"Aneuploidy; Loss of heterozygosity; Computer science; Computational biology; DNA sequencing; Hidden Markov model; Genome; Biology; Algorithm; Genetics; Artificial intelligence; Gene; Chromosome; Allele","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":[0.0006241772,0.0005512856,0.0005102516,0.0004616694,0.0002838438,0.0004744387,0.0007632123,0.0007762079,0.0005473376],"category_scores_gemma":[0.001379675,0.000342181,0.0007479927,0.0005341687,0.0003858824,0.0006336268,0.0004159049,0.0007008584,0.000137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009091191,"about_ca_system_score_gemma":0.001096444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01256644,"about_ca_topic_score_gemma":0.01522202,"domain_scores_codex":[0.9998336,0.00004061564,0.000008034526,0.00005014492,0.00004559298,0.00002197839],"domain_scores_gemma":[0.9995461,0.0002990457,0.00006921038,0.00002525446,0.00003692403,0.00002344041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003108533,0.00002178173,0.003135259,0.0000285853,0.00003117656,0.00004577966,0.00003049749,0.9795956,0.007311922,0.001928024,0.0001066383,0.007733675],"study_design_scores_gemma":[0.000001096456,0.000004120616,0.000278532,8.398359e-7,0.000003741683,0.000005873608,0.000001741849,0.9983588,0.0005385237,0.0007417459,0.00006227177,0.000002716725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1416064,0.000407095,0.8562026,0.0001758703,0.00002342764,0.00004573351,0.0003311936,0.0004645585,0.0007430963],"genre_scores_gemma":[0.8627496,0.0005252541,0.1339857,0.0001118202,0.00002929059,0.0001461837,0.0005972857,0.0001264813,0.001728397],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01256644,"threshold_uncertainty_score":0.02498662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04878853594419189,"score_gpt":0.2871448112280393,"score_spread":0.2383562752838474,"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."}}