{"id":"W2577435390","doi":"10.1182/blood.v122.21.1856.1856","title":"Using RNA-Seq, SNP-CN and Targeted Deep Sequencing To Improve The Diagnostic Paradigm In Multiple Myeloma","year":2013,"lang":"en","type":"article","venue":"Blood","topic":"Multiple Myeloma Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Ion semiconductor sequencing; Genetics; Biology; SNP array; DNA sequencing; Computational biology; Deep sequencing; Bioinformatics; Single-nucleotide polymorphism; Gene; Genome; Genotype","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":[0.005224275,0.0005939793,0.0006397649,0.0008148646,0.0005196807,0.001924583,0.001036785,0.0009723456,0.001635896],"category_scores_gemma":[0.004158899,0.0004416519,0.0007330306,0.000644675,0.0006223257,0.0007963136,0.001735225,0.001596808,0.0007296775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008257606,"about_ca_system_score_gemma":0.00103059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001223005,"about_ca_topic_score_gemma":0.004657214,"domain_scores_codex":[0.9977406,0.0008211996,0.0001164026,0.0006800142,0.0005079678,0.0001337844],"domain_scores_gemma":[0.9974834,0.001152358,0.0002617101,0.0003772959,0.000527948,0.0001972779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001015058,0.000177552,0.04489009,0.001752028,0.0007855165,0.0004580081,0.0006105619,0.01890686,0.687286,0.009008168,0.01721962,0.2178906],"study_design_scores_gemma":[0.0002662825,0.001128613,0.1143631,0.0008463547,0.0009253754,0.001910533,0.001016931,0.2148964,0.4463515,0.06218664,0.1557852,0.0003232252],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2705461,0.01202228,0.6743055,0.009206779,0.001225121,0.000664268,0.01693342,0.005383979,0.009712507],"genre_scores_gemma":[0.452213,0.003350402,0.522586,0.006853111,0.0004454512,0.0008643132,0.008974305,0.0009158641,0.003797462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005224275,"threshold_uncertainty_score":0.0276289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04256819058609661,"score_gpt":0.2937615958560791,"score_spread":0.2511934052699825,"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."}}