{"id":"W3035120337","doi":"10.1016/j.cancergen.2020.04.017","title":"13. Bioinformatics and interpretation for clinical reporting of an integrated whole genome and transcriptome assay","year":2020,"lang":"en","type":"article","venue":"Cancer Genetics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Transcriptome; Genome; Computational biology; Whole genome sequencing; Genomics; Bioinformatics; Personal genomics; Profiling (computer programming); Biology; Gene; Genetics; Computer science; Gene expression","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":[0.0003139354,0.0001217707,0.0002449449,0.00002038779,0.00003559462,0.0000294373,0.0000876182,0.000124243,0.000002618101],"category_scores_gemma":[0.000426973,0.000121217,0.00005820615,0.00005427706,0.0001076283,0.000005439023,0.00005002835,0.00006801781,1.959782e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009627299,"about_ca_system_score_gemma":0.0001315751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000155144,"about_ca_topic_score_gemma":0.00006902911,"domain_scores_codex":[0.9987112,0.00002546368,0.0008198982,0.0002486968,0.00005985413,0.0001349061],"domain_scores_gemma":[0.9990297,0.00003183362,0.0004719683,0.0001533587,0.0001618276,0.0001513262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007286985,0.00008342219,0.03072533,0.0006136093,0.0002505441,0.000002570858,0.002923102,0.002239684,0.6187185,0.00003810404,0.001035411,0.342641],"study_design_scores_gemma":[0.006429004,0.007408977,0.0657154,0.0001638514,0.0006338158,0.00003501643,0.002700178,0.3749904,0.1634605,0.0003190014,0.3766999,0.001443874],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9459456,0.004839721,0.04810688,0.0002635088,0.000144899,0.0002831556,0.00037397,0.000007125316,0.00003518801],"genre_scores_gemma":[0.9789256,0.005218442,0.01440885,0.0008776599,0.0002405834,0.00002509321,0.0002637271,0.00002414631,0.00001588208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.455258,"threshold_uncertainty_score":0.4943087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0407971360093999,"score_gpt":0.3382503439421368,"score_spread":0.2974532079327369,"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."}}