{"id":"W2121109569","doi":"10.1186/1471-2105-9-261","title":"Probe-specific mixed-model approach to detect copy number differences using multiplex ligation-dependent probe amplification (MLPA)","year":2008,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Instituto de Salud Carlos III; Generalitat de Catalunya; European Commission","keywords":"Multiplex ligation-dependent probe amplification; Normalization (sociology); Mixed model; Multiplex; Computer science; Copy-number variation; Bivariate analysis; Statistics; Algorithm; Mathematics; Biology; Machine learning; Bioinformatics; Genetics; Genome","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":[0.0001577021,0.0002378084,0.0001953444,0.00006216971,0.0003255254,0.00007858204,0.0002773184,0.0001810385,0.00001115113],"category_scores_gemma":[0.00003611376,0.0002168082,0.00009258196,0.0001389592,0.00007763118,0.0000277984,0.0001293764,0.00009203864,0.00006712304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005475236,"about_ca_system_score_gemma":0.0001917523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001988268,"about_ca_topic_score_gemma":0.00001700993,"domain_scores_codex":[0.9985996,0.00003406435,0.0005382622,0.0002623016,0.0002518843,0.0003139146],"domain_scores_gemma":[0.9990346,0.00001206177,0.0002130796,0.0004414546,0.0001744683,0.0001243263],"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":[0.0007168929,0.001735956,0.2632512,0.002085734,0.0005379485,0.000003570184,0.0276925,0.2817417,0.3925803,0.0147239,0.008314481,0.006615774],"study_design_scores_gemma":[0.00233418,0.0003421871,0.03190166,0.00009015259,0.00009066647,0.0004164568,0.004464404,0.7621084,0.1907676,0.001034754,0.004474493,0.001975127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4072434,0.00003371002,0.5910254,0.000009416354,0.00007195511,0.000435442,0.00004713461,0.0000245978,0.001109046],"genre_scores_gemma":[0.607031,0.00004463029,0.392193,0.00005187296,0.00008820293,0.00006983128,0.0001733665,0.00001605339,0.0003319605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4803667,"threshold_uncertainty_score":0.8841181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0513519044060402,"score_gpt":0.2478012337616608,"score_spread":0.1964493293556206,"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."}}