High-throughput gene expression technologies based on DNA microarrays allow the examination of biological systems. However, the interpretation of the complex molecular descriptions generated by these approaches is still challenging. The development of new methodologies to identify common regulatory mechanisms involved in the control of the expression of a set of co-expressed genes might enhance our capacity to extract functional information from genomic data sets. In this chapter, we describe a method that integrates different sources of information: gene expression data, genome sequence information, described transcription factor binding sites (TFBSs), functional information, and bibliographic data. The starting point of the analysis is the extraction of promoter sequences from a whole genome and the detection of TFBSs in each gene promoter. This information allows the identification of enriched TFBSs in the proximal promoter of differentially expressed genes. The functional and bibliographic interpretation of the results improves our biological insight into the regulatory mechanisms involved in a microarray experiment.
[Show abstract][Hide abstract] ABSTRACT: Predicting the course of multiple sclerosis (MS) on an individual basis is rather limited. However, promising results have been obtained in other multifactorial diseases through the use of biomarkers and clinical variables using computational classifiers. One of the high-throughput technologies in clinical diagnostic and personalized medicine is the DNA microarrays, which are used to measure the expression level of genes and microRNAs. The aim of this work is to review the literature regarding data mining techniques, especially classifiers, in the context of MS.
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