{"id":"W2099969687","doi":"10.1371/journal.pcbi.0030012","title":"From Bytes to Bedside: Data Integration and Computational Biology for Translational Cancer Research","year":2007,"lang":"en","type":"review","venue":"PLoS Computational Biology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Data science; Translational research; Translational science; Standardization; Computer science; Translational bioinformatics; Computational biology; Biological data; Systems biology; Computational model; Medical research; Genomics; Data integration; Modelling biological systems; Bioinformatics; Biology; Genome; Data mining; Artificial intelligence; Medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005590831,0.0003465922,0.0007094058,0.000310544,0.000194567,0.00005319776,0.0005649064,0.0005657708,0.00004109925],"category_scores_gemma":[0.0005242385,0.0003249091,0.0001253682,0.0002155787,0.0002615641,0.000006836478,0.0004089347,0.0002661732,0.00001677344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000618745,"about_ca_system_score_gemma":0.0009164532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001692971,"about_ca_topic_score_gemma":0.0002547627,"domain_scores_codex":[0.9974215,0.000220478,0.0006509463,0.001152576,0.0001630827,0.0003914067],"domain_scores_gemma":[0.9963289,0.00239685,0.000192735,0.0003689769,0.0005592458,0.0001532377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001456478,0.0001402929,0.0001117025,0.0005236971,0.0007015129,0.000001116584,0.00006714201,0.001647857,0.0004726913,0.009165567,0.009017903,0.9780049],"study_design_scores_gemma":[0.0004564191,0.000305808,0.00005802636,0.0004167405,0.0001774905,0.000006858242,0.00001254354,0.0028933,0.00003690767,0.02455914,0.9706682,0.0004085569],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000599746,0.8726273,0.1030856,0.0005401729,0.0003821602,0.001247925,0.02145736,0.00001446289,0.00004530199],"genre_scores_gemma":[0.001318328,0.7791948,0.05229456,0.0005386398,0.002620645,0.0004466347,0.1634731,0.0000750606,0.00003832353],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9775963,"threshold_uncertainty_score":0.9999203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2505620609345985,"score_gpt":0.484644968402939,"score_spread":0.2340829074683405,"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."}}