{"id":"W1629202041","doi":"10.3390/microarrays4030389","title":"Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery","year":2015,"lang":"en","type":"review","venue":"Microarrays","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research","keywords":"Normalization (sociology); Data integration; Computer science; Biomarker discovery; Database normalization; Robustness (evolution); Microarray analysis techniques; Genomics; Microarray databases; Data mining; Data science; Computational biology; Bioinformatics; Biology; Machine learning; Proteomics; Cluster analysis; Gene; Genome","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006710681,0.001499314,0.003812126,0.005103507,0.0003604458,0.002699407,0.001940066,0.001650593,0.002442853],"category_scores_gemma":[0.006739555,0.0006853264,0.002142906,0.006791263,0.0009822751,0.002004243,0.00141711,0.002746849,0.001501323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001539005,"about_ca_system_score_gemma":0.002676783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001537207,"about_ca_topic_score_gemma":0.002121948,"domain_scores_codex":[0.9979438,0.0006920634,0.0001976601,0.000409369,0.0006763577,0.00008074613],"domain_scores_gemma":[0.9954575,0.003206652,0.0004068368,0.0002231017,0.0006202662,0.00008566138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.00009405626,0.00005433062,0.001274656,0.03115234,0.001354858,0.0002352204,0.0001581198,0.001973308,0.00544372,0.01835509,0.0165708,0.9233335],"study_design_scores_gemma":[0.00004324506,0.0002103412,0.00516496,0.01219061,0.002105398,0.001918979,0.0002104917,0.002883951,0.006758379,0.04117024,0.9271275,0.0002158647],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003794295,0.9794159,0.01642512,0.001244058,0.0003790436,0.00004952387,0.0002524194,0.0001297514,0.001724696],"genre_scores_gemma":[0.004650419,0.9748061,0.01790597,0.0009116695,0.0004531788,0.0001246615,0.0004428692,0.00004022616,0.0006648996],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006710681,"threshold_uncertainty_score":0.03548986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1477336557429385,"score_gpt":0.3720162867200132,"score_spread":0.2242826309770747,"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."}}