{"id":"W2159109545","doi":"10.1109/tcbb.2006.29","title":"A Powerful Approach for Effective Finding of Significantly Differentially Expressed Genes","year":2006,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Estimator; Computer science; Set (abstract data type); Extension (predicate logic); Data mining; Expression (computer science); Mathematics; Statistics","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":[0.0001033721,0.0001390806,0.0001492859,0.0001114134,0.0001319284,0.00001415056,0.0001194289,0.0001648961,0.000005276284],"category_scores_gemma":[0.00001217238,0.0001192642,0.00008930878,0.00008032528,0.000118385,0.000007733049,0.000005641712,0.00006144827,0.00000106913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008869139,"about_ca_system_score_gemma":0.0000459141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003885962,"about_ca_topic_score_gemma":0.000002018773,"domain_scores_codex":[0.9992516,0.00004148765,0.0003024041,0.0001947273,0.00007481662,0.0001349474],"domain_scores_gemma":[0.9994375,0.00009767834,0.000151168,0.0001571387,0.000121356,0.00003523208],"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":[0.0006793726,0.0004577009,0.0005506991,0.0002309586,0.0001816244,1.091348e-7,0.0001755453,0.0500143,0.8868928,0.0009852452,0.0006755454,0.05915611],"study_design_scores_gemma":[0.004084294,0.001832684,0.00764722,0.00005586477,0.0001395205,0.00001804822,0.0004497937,0.08280926,0.8906218,0.008369953,0.003286994,0.0006845836],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1435517,0.00008848398,0.85539,0.00005648399,0.0001260978,0.000376693,0.0001990832,0.00001176873,0.0001996358],"genre_scores_gemma":[0.9367822,0.00004052394,0.06234847,0.00006737341,0.00006176767,0.0000833684,0.0005245639,0.000008547706,0.00008317128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7932305,"threshold_uncertainty_score":0.4863454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0136051692005075,"score_gpt":0.267341398799764,"score_spread":0.2537362295992565,"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."}}