{"id":"W4281845612","doi":"10.1016/j.annonc.2022.05.522","title":"Whole-genome and transcriptome analysis enhances precision cancer treatment options","year":2022,"lang":"en","type":"article","venue":"Annals of Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":120,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Centre for Applied Research in Cancer Control; Simon Fraser University; Kelowna General Hospital; Pancreas Centre (Canada); University of British Columbia; Canada's Michael Smith Genome Sciences Centre","funders":"Canada Research Chairs; Genome British Columbia; Canada Foundation for Innovation; National Institutes of Health; BC Cancer Foundation; Genome Canada","keywords":"Medicine; Precision medicine; Transcriptome; Personalized medicine; Clinical trial; Genome; Computational biology; Bioinformatics; Gene; Oncology; Internal medicine; Gene expression; Genetics; Biology; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001917697,0.0003982315,0.0007071081,0.001132887,0.0002426832,0.001311201,0.0002818257,0.0006251798,0.003193623],"category_scores_gemma":[0.002433989,0.0002138543,0.0005499332,0.0008011592,0.0004224137,0.0008490623,0.0008545874,0.0009266908,0.0008556262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004798712,"about_ca_system_score_gemma":0.0006168128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004857588,"about_ca_topic_score_gemma":0.0009959978,"domain_scores_codex":[0.9992095,0.0002741911,0.00005211588,0.0002153672,0.0001899306,0.00005876884],"domain_scores_gemma":[0.9985105,0.0006065852,0.0003101824,0.0002505634,0.0002362274,0.0000858435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001485084,0.0003119977,0.1525142,0.001217435,0.0006121268,0.0005444774,0.0004681819,0.01105095,0.3244918,0.005144452,0.01300121,0.489158],"study_design_scores_gemma":[0.0002326814,0.001269824,0.5817231,0.0006007938,0.001396653,0.003438338,0.0009146914,0.03183626,0.1712877,0.02881378,0.1783366,0.0001495744],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6526079,0.03532176,0.2385594,0.01231935,0.0004735554,0.0005265436,0.01778065,0.002504216,0.03990671],"genre_scores_gemma":[0.9067309,0.01154373,0.06813129,0.003191767,0.0003770274,0.0003214603,0.006239665,0.0003486311,0.003115479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003193623,"threshold_uncertainty_score":0.01068372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04145877125295756,"score_gpt":0.3570804674799838,"score_spread":0.3156216962270262,"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."}}