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CMEP: a database for circulating microRNA expression profiling.
Li, Jian-Rong; Tong, Chun-Yip; Sung, Tsai-Jung; Kang, Ting-Yu; Zhou, Xianghong Jasmine; Liu, Chun-Chi.
Afiliação
  • Li JR; Institute of Genomics and Bioinformatics.
  • Tong CY; Advanced Plant Biotechnology Center, National Chung Hsing University, Taichung City 402, Taiwan.
  • Sung TJ; Institute of Genomics and Bioinformatics.
  • Kang TY; Institute of Genomics and Bioinformatics.
  • Zhou XJ; Institute of Genomics and Bioinformatics.
  • Liu CC; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.
Bioinformatics ; 35(17): 3127-3132, 2019 09 01.
Article em En | MEDLINE | ID: mdl-30668638
ABSTRACT
MOTIVATION In recent years, several experimental studies have revealed that the microRNAs (miRNAs) in serum, plasma, exosome and whole blood are dysregulated in various types of diseases, indicating that the circulating miRNAs may serve as potential noninvasive biomarkers for disease diagnosis and prognosis. However, no database has been constructed to integrate the large-scale circulating miRNA profiles, explore the functional pathways involved and predict the potential biomarkers using feature selection between the disease conditions. Although there have been several studies attempting to generate a circulating miRNA database, they have not yet integrated the large-scale circulating miRNA profiles or provided the biomarker-selection function using machine learning methods.

RESULTS:

To fill this gap, we constructed the Circulating MicroRNA Expression Profiling (CMEP) database for integrating, analyzing and visualizing the large-scale expression profiles of phenotype-specific circulating miRNAs. The CMEP database contains massive datasets that were manually curated from NCBI GEO and the exRNA Atlas, including 66 datasets, 228 subsets and 10 419 samples. The CMEP provides the differential expression circulating miRNAs analysis and the KEGG functional pathway enrichment analysis. Furthermore, to provide the function of noninvasive biomarker discovery, we implemented several feature-selection methods, including ridge regression, lasso regression, support vector machine and random forests. Finally, we implemented a user-friendly web interface to improve the user experience and to visualize the data and results of CMEP. AVAILABILITY AND IMPLEMENTATION CMEP is accessible at http//syslab5.nchu.edu.tw/CMEP.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Bases de Dados Factuais Tipo de estudo: Prognostic_studies Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Bases de Dados Factuais Tipo de estudo: Prognostic_studies Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2019 Tipo de documento: Article