{"id":"W1993548875","doi":"10.1021/pr500352e","title":"Integrating Meta-Analysis of Microarray Data and Targeted Proteomics for Biomarker Identification: Application in Breast Cancer","year":2014,"lang":"en","type":"review","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Canada Research Chairs; University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Breast cancer; Biomarker; Proteome; Biomarker discovery; Proteomics; Cancer; Transcriptome; Biology; Computational biology; Cancer biomarkers; Tissue microarray; Gene expression profiling; Bioinformatics; Oncology; Cancer research; Gene expression; Gene; Medicine; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003914467,0.001380364,0.003354263,0.004887917,0.000191116,0.001567427,0.001233099,0.0009803623,0.0007368732],"category_scores_gemma":[0.002888576,0.0006445822,0.001632344,0.005916684,0.0004324857,0.001106264,0.000776155,0.001144047,0.0004581273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008163704,"about_ca_system_score_gemma":0.001052904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009861107,"about_ca_topic_score_gemma":0.001469277,"domain_scores_codex":[0.9989169,0.0003336977,0.00007658463,0.0002253377,0.0003871211,0.00006046654],"domain_scores_gemma":[0.9985816,0.0007557753,0.000206872,0.00008306083,0.0003154969,0.00005713072],"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":[0.0001615555,0.00008245287,0.004244518,0.01225276,0.002529775,0.0002861893,0.00007267459,0.002394241,0.01111231,0.002568914,0.005298981,0.9589956],"study_design_scores_gemma":[0.0002559621,0.001912527,0.07599424,0.01138404,0.01356747,0.01068968,0.0007259327,0.03610617,0.05795904,0.0495901,0.741066,0.000748902],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001675511,0.9879456,0.009087694,0.0003646716,0.0001431021,0.00002657386,0.0001179225,0.0000799155,0.0005590117],"genre_scores_gemma":[0.02099733,0.9542487,0.02311499,0.0003784867,0.0002910367,0.00007050185,0.0003023488,0.00002604214,0.0005706276],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004887917,"threshold_uncertainty_score":0.02070189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2562993523114034,"score_gpt":0.5044482529406321,"score_spread":0.2481489006292287,"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."}}