{"id":"W2082220672","doi":"10.1186/1471-2105-8-358","title":"Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements","year":2007,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Ontario Genomics; Ontario Genomics Institute; Genome Canada","keywords":"Arabidopsis thaliana; DNA microarray; Computational biology; Gene expression; Biology; Gene; Genetics; Function (biology); Arabidopsis; Scale (ratio); Mutant; Geography; Cartography","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":[0.0009590497,0.0001907388,0.0001597399,0.0002063912,0.0001306475,0.00003824569,0.0001921468,0.0002266024,0.00001700942],"category_scores_gemma":[0.00007788262,0.0001833925,0.00007466305,0.0003107143,0.00002537024,0.00002240362,0.0001130836,0.0001074306,0.0000169558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009409346,"about_ca_system_score_gemma":0.0001061379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005275948,"about_ca_topic_score_gemma":0.00005617472,"domain_scores_codex":[0.9983774,0.00005479364,0.0005429452,0.0002624754,0.0003541319,0.0004082449],"domain_scores_gemma":[0.9990699,0.000009696249,0.0001954154,0.0004348278,0.0001029864,0.0001872284],"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":[0.0003221649,0.00008700223,0.04096103,0.00004538122,0.00001615857,4.332955e-7,0.0004429037,0.002407484,0.9520893,0.00000795075,0.00156998,0.002050206],"study_design_scores_gemma":[0.001741638,0.0002065475,0.03310456,0.0001169492,0.00003359889,0.000008459181,0.002190132,0.01893952,0.935503,0.0000327017,0.007719243,0.0004036288],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.402088,0.00010402,0.5959606,0.0000127749,0.0003595668,0.0002690218,0.0000126957,0.0000201283,0.001173157],"genre_scores_gemma":[0.8596247,0.00002622887,0.139076,0.0005377739,0.0001865799,0.00002695488,0.0002165641,0.00003174858,0.0002734349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4575367,"threshold_uncertainty_score":0.747853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04853023466139118,"score_gpt":0.2851730843073981,"score_spread":0.236642849646007,"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."}}