{"id":"W2062635956","doi":"10.1073/pnas.1106233109","title":"Reducing system noise in copy number data using principal components of self-self hybridizations","year":2011,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Spurious relationship; Noise (video); False positive paradox; Copy-number variation; Principal component analysis; Genetics; Biology; Computational biology; Computer science; Genome; Pattern recognition (psychology); Artificial intelligence; Machine learning; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003332281,0.001000407,0.0006831202,0.001500046,0.0004567319,0.001293945,0.0005777086,0.0006113045,0.001044818],"category_scores_gemma":[0.01683817,0.0003720437,0.0008953289,0.001733443,0.0009411523,0.001060857,0.0007484808,0.001519886,0.0005264881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005529022,"about_ca_system_score_gemma":0.0008699479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001522747,"about_ca_topic_score_gemma":0.002255678,"domain_scores_codex":[0.9981338,0.0007278619,0.0001187816,0.000415667,0.0004911304,0.0001126893],"domain_scores_gemma":[0.9931384,0.004171929,0.0005637097,0.001210898,0.0008030994,0.0001120267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007597154,0.0003819205,0.02785808,0.0005010045,0.0004872443,0.0003057035,0.0009111913,0.0979082,0.2006483,0.01403559,0.002090766,0.6541124],"study_design_scores_gemma":[0.00004180465,0.0003190551,0.05444916,0.00003773002,0.0001519179,0.0004333706,0.0001506825,0.8391242,0.08076884,0.02049424,0.003892764,0.0001362516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1302594,0.0002396257,0.8668239,0.0002191934,0.00005379419,0.00009968929,0.0001982133,0.001484773,0.0006213835],"genre_scores_gemma":[0.4205189,0.0002774915,0.5768761,0.00007979381,0.00004761745,0.0001601781,0.00089093,0.0003362665,0.0008127412],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003332281,"threshold_uncertainty_score":0.01762301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07384774245820208,"score_gpt":0.2955817937608162,"score_spread":0.2217340513026141,"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."}}