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Bioinformatics. 2019 Jan 22. doi: 10.1093/bioinformatics/btz042. [Epub ahead of print]

CMEP: a database for circulating microRNA expression profiling.

Author information

1
Institute of Genomics and Bioinformatics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City, Taiwan.
2
Advanced Plant Biotechnology Center, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City, Taiwan.
3
Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, 611 Charles E Young Dr E, Los Angeles, CA, USA.

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:

CMEP is accessible at http://syslab5.nchu.edu.tw/CMEP.

Supplementary information:

Supplementary data are available at Bioinformatics online.

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