{"id":"W2115059509","doi":"10.1186/1471-2105-15-47","title":"PSEUDOMARKER 2.0: efficient computation of likelihoods using NOMAD","year":2014,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"FP7 Health; Air Force Office of Scientific Research; National Institute of Mental Health; National Institute on Aging; National Institutes of Health; Academy of Finland; European Commission","keywords":"Computation; Computer science; Computational biology; DNA microarray; Biology; Algorithm; Genetics; Gene expression; Gene","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":[0.0004686921,0.00009753004,0.000160335,0.00004597724,0.00005641508,0.000007421327,0.00009496069,0.000132131,0.000004045678],"category_scores_gemma":[0.0002579052,0.00008958381,0.00008014202,0.00007998635,0.00005458856,0.000001956018,0.00006982328,0.00003822533,0.00001037745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001141543,"about_ca_system_score_gemma":0.00005558482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008994463,"about_ca_topic_score_gemma":0.000004655099,"domain_scores_codex":[0.9991523,0.0000705501,0.0004195432,0.00009580562,0.00009230163,0.0001695075],"domain_scores_gemma":[0.9993553,0.00003395403,0.0002684739,0.0001898904,0.0001068776,0.00004554049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001000195,0.0002672975,0.1741921,0.0004640228,0.0001698873,1.573124e-7,0.0005646641,0.7626061,0.03280023,0.0009502261,0.004053607,0.02383167],"study_design_scores_gemma":[0.0004029499,0.0001133423,0.03376798,0.00001269711,0.00002093777,0.000006355814,0.000112736,0.9633881,0.001222015,0.00008661652,0.0007473948,0.0001188364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4788,0.00003496512,0.5197091,0.00001032549,0.00008688489,0.000065146,0.000004824139,0.000004701836,0.001284106],"genre_scores_gemma":[0.7375662,0.000006403046,0.2621925,0.00009537188,0.00005737216,0.000001968336,0.00004802904,0.000007090096,0.00002502435],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2587663,"threshold_uncertainty_score":0.3653122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01786271683825971,"score_gpt":0.2715878407092353,"score_spread":0.2537251238709756,"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."}}