{"id":"W2019000099","doi":"10.1186/1471-2105-12-205","title":"A scan statistic to extract causal gene clusters from case-control genome-wide rare CNV data","year":2011,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; SickKids Foundation","funders":"","keywords":"Scan statistic; False positive paradox; Cluster analysis; Computational biology; Statistic; Statistical power; Statistical hypothesis testing; Computer science; Data mining; Test statistic; Gene; Genome; False discovery rate; Genetics; Biology; Statistics; Artificial intelligence; Mathematics","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":[0.0002093763,0.0002377801,0.0002140988,0.00006424336,0.0001524837,0.00007377682,0.0005363876,0.0001404171,0.0002218547],"category_scores_gemma":[0.0001384645,0.0002253264,0.00005829836,0.0000774123,0.00005215745,0.00003073069,0.0003615011,0.00008728611,0.0001343735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003392881,"about_ca_system_score_gemma":0.0002038396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005270262,"about_ca_topic_score_gemma":0.00114953,"domain_scores_codex":[0.9986303,0.00004911971,0.0005286755,0.000282909,0.0001414458,0.0003675325],"domain_scores_gemma":[0.9983751,0.0000464991,0.0001750457,0.001106408,0.00008357492,0.000213441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008487252,0.003770237,0.4580294,0.003890912,0.01086604,0.005280625,0.1407354,0.03489006,0.07322542,0.00252856,0.1630786,0.0952175],"study_design_scores_gemma":[0.02090112,0.004848881,0.2928975,0.000270747,0.002805567,0.008690302,0.05283296,0.4143911,0.03563909,0.001301472,0.1560564,0.009364874],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.424101,0.0002244911,0.5650419,0.00003401787,0.0002652841,0.0005177736,0.008270309,0.00003371303,0.001511516],"genre_scores_gemma":[0.748897,0.00002380288,0.2473859,0.000677536,0.0001851246,0.000025236,0.002514134,0.00002803181,0.0002632364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.379501,"threshold_uncertainty_score":0.9188542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03702735802862018,"score_gpt":0.2382466309077363,"score_spread":0.2012192728791161,"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."}}