title: Analysis of microRNA expression using machine learning.
abstract: The systematic analysis of miRNA expression and its potential mRNA targets constitutes a basal objective in miRNA research in addition to miRNA gene detection and miRNA target prediction. In this chapter we address methodical issues of miRNA expression analysis using self-organizing maps (SOM), a neural network machine learning algorithm with strong visualization and second-level analysis capabilities widely used to categorize large-scale, high-dimensional data. We shortly review selected experimental and theoretical aspects of miRNA expression analysis. Then, the protocol of our SOM method is outlined with special emphasis on miRNA/mRNA coexpression. The method allows extracting differentially expressed RNA transcripts, their functional context, and also characterization of global properties of expression states and profiles. In addition to the separate study of miRNA and mRNA expression landscapes, we propose the combined analysis of both entities using a covariance SOM.
authors.affiliation: Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.
authors.forename: Henry
authors.initials: H
authors.lastname: Wirth
authors.forename: Mehmet Volkan
authors.initials: MV
authors.lastname: Cakir
authors.forename: Lydia
authors.initials: L
authors.lastname: Hopp
authors.forename: Hans
authors.initials: H
authors.lastname: Binder
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002138:Calibration; D035683:MicroRNAs / Q000235:genetics*
chemical_list: D035683:MicroRNAs