{"id":"W2151925986","doi":"10.1186/gb-2008-9-7-r118","title":"MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis","year":2008,"lang":"en","type":"article","venue":"Genome biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bioengineering; National Institute of Environmental Health Sciences; Office of Naval Research; National Heart, Lung, and Blood Institute; National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Bioconductor; Graphical user interface; Computer science; Interface (matter); Java; Operating system; Programming language; User interface; Software engineering; Chemistry","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":[0.0001072996,0.0001381404,0.0002072172,0.0002405041,0.00009927474,0.000006692982,0.0002111532,0.0002363536,0.00004241727],"category_scores_gemma":[0.00002485117,0.000124973,0.0001584752,0.0004446916,0.000137615,0.00000280065,0.00006168338,0.00006595504,0.000006909692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002656577,"about_ca_system_score_gemma":0.00008810944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003914467,"about_ca_topic_score_gemma":0.00004378259,"domain_scores_codex":[0.9989155,0.00006939587,0.0002470254,0.0004961838,0.0000369871,0.0002349335],"domain_scores_gemma":[0.9993705,0.00001296987,0.0000949666,0.0003770125,0.00007358626,0.00007097454],"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":[0.00004761337,0.00004976329,0.01234568,0.000004504746,0.0001157408,1.981663e-7,0.00004812625,0.00007637173,0.986845,0.0003093446,0.00007070924,0.00008691948],"study_design_scores_gemma":[0.0009326644,0.0002496576,0.02168415,0.000003117855,0.0001381045,0.00001883014,0.000215313,0.0001136029,0.5109455,0.0004257089,0.4648529,0.000420481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9333676,0.001257437,0.06465641,0.0001357901,0.00006648253,0.0003816386,0.00005232194,0.00001037172,0.00007200633],"genre_scores_gemma":[0.9958962,0.0002169281,0.002654154,0.0001756558,0.0001224066,0.0002126077,0.0004098309,0.00001479405,0.0002974517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4758995,"threshold_uncertainty_score":0.5096249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03100668269209644,"score_gpt":0.320252608942963,"score_spread":0.2892459262508666,"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."}}