{"id":"W2120027330","doi":"10.1186/1471-2105-10-306","title":"A methodology for the analysis of differential coexpression across the human lifespan","year":2009,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of General Medical Sciences; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Biology; DNA microarray; Similarity (geometry); Differential (mechanical device); Computational biology; Gene; Microarray analysis techniques; Gene expression; Genetics; Computer science; Artificial intelligence","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":[0.0007229,0.0001587559,0.0002769759,0.00004449066,0.0003359794,0.00004764584,0.0005074979,0.0001850415,0.00001126712],"category_scores_gemma":[0.00009630698,0.0000839161,0.0003602073,0.0002106076,0.000156863,0.000006043805,0.0001340288,0.0001043779,0.000001390464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006336613,"about_ca_system_score_gemma":0.00003291306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007391568,"about_ca_topic_score_gemma":0.00006798933,"domain_scores_codex":[0.9988411,0.00005760305,0.0005714913,0.0001060348,0.0001308778,0.0002929041],"domain_scores_gemma":[0.9986629,0.0001880074,0.0003755802,0.0006329589,0.00009465294,0.00004592589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002331568,0.0009369343,0.009420879,0.0009472584,0.01334342,7.250544e-7,0.03422209,0.1013966,0.2692196,0.03592874,0.08344042,0.4488118],"study_design_scores_gemma":[0.002275117,0.0008954931,0.04273501,0.00003429854,0.002292996,0.00001032481,0.004485891,0.8892426,0.02238248,0.0015375,0.03347085,0.0006374313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.163247,0.0002593733,0.8352045,0.0002059585,0.0001689019,0.0005018458,0.0001245315,0.00001006396,0.0002778602],"genre_scores_gemma":[0.958703,0.00008664669,0.03949214,0.0008161509,0.0002401257,0.00003029657,0.0003624776,0.00001081673,0.0002583791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7957124,"threshold_uncertainty_score":0.3421999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04775446544068047,"score_gpt":0.3403412361732412,"score_spread":0.2925867707325608,"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."}}