Chapter title |
Identification of Potential MicroRNA Biomarkers by Meta-analysis
|
---|---|
Chapter number | 24 |
Book title |
Computational Drug Discovery and Design
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7756-7_24 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7755-0, 978-1-4939-7756-7
|
Authors |
Hongmei Zhu, Siu-wai Leung |
Abstract |
Meta-analysis statistically assesses the results (e.g., effect sizes) across independent studies that are conducted in accordance with similar protocols and objectives. Current genomic meta-analysis studies do not perform extensive re-analysis on raw data because full data access would not be commonplace, although the best practice of open research for sharing well-formed data have been actively advocated. This chapter describes a simple and easy-to-follow method for conducting meta-analysis of multiple studies without using raw data. Examples for meta-analysis of microRNAs (miRNAs) are provided to illustrate the method. MiRNAs are potential biomarkers for early diagnosis and epigenetic monitoring of diseases. A number of miRNAs have been identified to be differentially expressed, i.e., overexpressed or underexpressed, under diseased states but only a small fraction would be highly effective biomarkers or therapeutic targets of diseases. The meta-analysis method as described in this chapter aims to identify the miRNAs that are consistently found dysregulated across independent studies as biomarkers. |
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