{"id":"W2555040994","doi":"10.21037/tcr.2016.10.30","title":"The challenge for precision medicine: all tumor genomes are different and all cancer patients are different in their own way","year":2016,"lang":"en","type":"article","venue":"Translational Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Epigenomics; Genome; Malignancy; Genome instability; Cancer; Human genome; Biology; Gene; Metastasis; Computational biology; Disease; Genomics; Whole genome sequencing; Genetics; Bioinformatics; Medicine; DNA methylation; Internal medicine; DNA; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03513951,0.001449949,0.004385149,0.002702396,0.003359714,0.008696388,0.003799033,0.0125454,0.008915819],"category_scores_gemma":[0.05087799,0.0008602513,0.001600659,0.002134857,0.02248066,0.0204816,0.008349857,0.02583241,0.004052529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005558277,"about_ca_system_score_gemma":0.008131136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002680198,"about_ca_topic_score_gemma":0.001880138,"domain_scores_codex":[0.9844313,0.005402929,0.0008310064,0.003166808,0.005459187,0.0007087897],"domain_scores_gemma":[0.9235157,0.05313296,0.002964973,0.008749843,0.00809046,0.003546146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005443599,0.000152053,0.003775109,0.003185484,0.0007155667,0.0005717152,0.001772855,0.001749024,0.004390301,0.3439339,0.29744,0.3417697],"study_design_scores_gemma":[0.00007818924,0.0001977516,0.001670072,0.001455919,0.0001842899,0.001260994,0.0007950762,0.0008191557,0.001096215,0.5906833,0.4016113,0.0001478142],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.002468708,0.2130902,0.05333747,0.7064855,0.01252649,0.00005027113,0.0006785947,0.0006476101,0.0107152],"genre_scores_gemma":[0.1222459,0.2752218,0.08093302,0.4452834,0.06496357,0.0004186326,0.001465567,0.0005588317,0.008909277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03513951,"threshold_uncertainty_score":0.1858377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07257766006844872,"score_gpt":0.3662885175921337,"score_spread":0.293710857523685,"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."}}