{"id":"W1551731376","doi":"10.4137/cin.s867","title":"A Biological Evaluation of Six Gene Set Analysis Methods for Identification of Differentially Expressed Pathways in Microarray Data","year":2008,"lang":"en","type":"article","venue":"Cancer Informatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Cancer Institute","keywords":"Microarray analysis techniques; Computational biology; Biological pathway; Gene; Identification (biology); Gene expression profiling; Microarray databases; Biology; Gene expression; Gene chip analysis; Set (abstract data type); Microarray; DNA microarray; Phenotype; Genetics; Bioinformatics; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.00144656,0.0001076363,0.0002774626,0.0001074678,0.00003338852,0.00000783974,0.0003856484,0.0001543272,0.00001121041],"category_scores_gemma":[0.0001408206,0.00009358407,0.00009838605,0.0002253943,0.00008472369,0.00001475665,0.0001402436,0.00004771656,2.988359e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002142803,"about_ca_system_score_gemma":0.0001685893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001319781,"about_ca_topic_score_gemma":0.00002586943,"domain_scores_codex":[0.9985892,0.00007434378,0.0009325356,0.0001248654,0.0001382752,0.0001407429],"domain_scores_gemma":[0.9984075,0.00003458788,0.0005945599,0.0006344191,0.0002974536,0.00003141882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001378728,0.00008514167,0.003229819,0.0001875728,0.0005113138,4.451548e-8,0.002067514,0.02105143,0.9290609,0.0000475717,0.0007119997,0.04290884],"study_design_scores_gemma":[0.001323196,0.00009768727,0.006966709,0.00001793726,0.0004428708,0.000001504111,0.000379207,0.535163,0.4540914,0.0002307488,0.001077876,0.0002078389],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6159821,0.000545349,0.3824431,0.000006372945,0.00007107942,0.0003148665,0.0005985164,0.000002149016,0.00003651538],"genre_scores_gemma":[0.9548463,0.000863665,0.04098898,0.0000348834,0.00004055555,0.00006632622,0.003146147,0.000005875185,0.000007214763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5141116,"threshold_uncertainty_score":0.3816247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1293348487597459,"score_gpt":0.380155960850302,"score_spread":0.2508211120905561,"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."}}