{"id":"W1978034558","doi":"10.4061/2009/869093","title":"Data Integration in Genetics and Genomics: Methods and Challenges","year":2009,"lang":"en","type":"article","venue":"Human Genomics and Proteomics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":150,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Canada; Canadian Institutes of Health Research; Mitacs; Ontario Genomics; Ontario Genomics Institute","keywords":"Genomics; Computational biology; Data integration; Proteomics; Genome; Biology; Data type; Functional genomics; Data science; Computer science; Gene; Data mining; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004182961,0.0001363702,0.0001341947,0.00006200419,0.00008771691,0.00006122563,0.0001533247,0.0001420372,0.000001658169],"category_scores_gemma":[0.00001903258,0.000136672,0.00001130542,0.00002717215,0.00006367906,0.000008006726,0.0001784251,0.00009350715,2.646665e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001455254,"about_ca_system_score_gemma":0.00003184109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000454526,"about_ca_topic_score_gemma":0.00006038449,"domain_scores_codex":[0.9990768,0.00007109039,0.0002048941,0.0004746114,0.00003653803,0.0001360425],"domain_scores_gemma":[0.9993982,0.000006233428,0.00008086915,0.0004206992,0.0000233108,0.00007067504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002902562,0.0000163882,0.0001584761,0.00001393276,0.0000054735,2.895927e-7,0.0002482613,0.00000438075,0.8112857,0.0009033817,0.00005008702,0.1872846],"study_design_scores_gemma":[0.003472345,0.00135548,0.123684,0.00009751377,0.00007713164,0.00008720352,0.001989195,0.01100792,0.5368026,0.02743725,0.2925914,0.001397901],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9687278,0.02484871,0.005018835,0.0006806461,0.00003725013,0.000344809,0.00002267348,0.000006053948,0.0003132126],"genre_scores_gemma":[0.8442074,0.08722685,0.06775837,0.0003109085,0.0001865314,0.00002010086,0.0001882644,0.00002141531,0.00008016051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2925413,"threshold_uncertainty_score":0.5573323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09538990105904042,"score_gpt":0.3624385403715629,"score_spread":0.2670486393125225,"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."}}