{"id":"W2766750745","doi":"10.1093/bioinformatics/btx707","title":"NanoStringNormCNV: pre-processing of NanoString CNV data","year":2017,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; Prostate Cancer Canada; Terry Fox Research Institute; Ontario Institute for Cancer Research; Movember Foundation","keywords":"Computer science; Normalization (sociology); Visualization; Software; Copy-number variation; Data mining; Data processing; Software package; R package; Database; Computational science; Operating system; Biology; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001847674,0.0001262592,0.0001494669,0.00003163183,0.0002356066,0.0001244097,0.001087559,0.0001103302,0.000004960581],"category_scores_gemma":[0.0004626907,0.0001229774,0.00004373637,0.00002638724,0.0001195384,0.0000277653,0.00105771,0.00005901682,0.000005931279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009399192,"about_ca_system_score_gemma":0.0001719206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000520695,"about_ca_topic_score_gemma":0.00004284183,"domain_scores_codex":[0.9991515,0.000004557824,0.00034449,0.000163943,0.0001335206,0.0002020491],"domain_scores_gemma":[0.9977429,0.00001116502,0.0004220941,0.001669098,0.00008652733,0.00006821912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003559462,0.0002918127,0.0548696,0.001458118,0.000290804,0.000009895968,0.00119239,0.0004870552,0.2555753,0.0009420197,0.03217244,0.6523546],"study_design_scores_gemma":[0.002783067,0.0006032919,0.0349231,0.0003922019,0.0001556155,0.0000523135,0.000659078,0.02879141,0.6630433,0.0001856638,0.2672856,0.001125331],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9725662,0.001413353,0.01124891,0.0001508971,0.0006498715,0.0003236606,0.0005835813,0.00002212395,0.01304143],"genre_scores_gemma":[0.9871479,0.0005621141,0.01155345,0.00008211537,0.0001921751,0.000003048702,0.0002725298,0.00001637772,0.0001702571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6512293,"threshold_uncertainty_score":0.5014871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03027205727068864,"score_gpt":0.2912801544147864,"score_spread":0.2610080971440977,"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."}}