CMEP: a database for circulating microRNA expression profiling
| Autor: | Chun-Yip Tong, Jian-Rong Li, Ting-Yu Kang, Tsai-Jung Sung, Xianghong Jasmine Zhou, Chun-Chi Liu |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Databases Factual Feature selection Biology computer.software_genre Exosomes Biochemistry Exosome 03 medical and health sciences 0302 clinical medicine microRNA Circulating MicroRNA KEGG Molecular Biology 030304 developmental biology Noninvasive biomarkers 0303 health sciences Database Gene Expression Profiling Original Papers Microvesicles Computer Science Applications Gene expression profiling Computational Mathematics Computational Theory and Mathematics 030220 oncology & carcinogenesis computer Biomarkers |
| Zdroj: | Bioinformatics |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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