{"id":"W2539021942","doi":"10.1158/0008-5472.can-16-0860","title":"Big Data–Led Cancer Research, Application, and Insights","year":2016,"lang":"en","type":"article","venue":"Cancer Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University; Queen's University Belfast","keywords":"Big data; Key (lock); Cancer; Irish; Data science; Precision medicine; Computer science; Computational biology; Medicine; Biology; Data mining; Pathology; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02700648,0.000746814,0.0009170273,0.003328899,0.001115839,0.008227234,0.001605279,0.00242518,0.002529591],"category_scores_gemma":[0.02069486,0.0005569638,0.0009607011,0.005351961,0.005762964,0.008028816,0.004797236,0.00753146,0.0009420472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005750909,"about_ca_system_score_gemma":0.007360368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002542371,"about_ca_topic_score_gemma":0.003671415,"domain_scores_codex":[0.9930842,0.0041646,0.0002259394,0.0004162436,0.001781401,0.0003274982],"domain_scores_gemma":[0.9748473,0.01841855,0.0005626022,0.001747081,0.002620493,0.001804055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001805692,0.000125907,0.005532767,0.002243186,0.0001753318,0.0003878269,0.0009751154,0.006391866,0.001916709,0.5342772,0.1140279,0.3337657],"study_design_scores_gemma":[0.00005164152,0.0000885688,0.002496628,0.001866759,0.00006120786,0.0003298122,0.001470732,0.007776921,0.002317088,0.5865396,0.3969269,0.00007415997],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.01008495,0.1604975,0.07015941,0.7247763,0.006370053,0.0001748445,0.001644577,0.0007111602,0.02558129],"genre_scores_gemma":[0.2418506,0.4879816,0.1406834,0.1019497,0.01370294,0.0006157159,0.002099823,0.0004327148,0.01068358],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02700648,"threshold_uncertainty_score":0.1428257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1133489117699033,"score_gpt":0.4133608200046128,"score_spread":0.3000119082347095,"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."}}