Titlebook: Microarray Bioinformatics; Verónica Bolón-Canedo,Amparo Alonso-Betanzos Book 2019 Springer Science+Business Media, LLC, part of Springer N

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Statistical Analysis of Microarray Data, analysis of microarray data can be done in different ways using different tools. In this chapter a typical workflow for analyzing microarray data using R and Bioconductor packages is presented. The workflow starts with the raw data—binary files obtained from the hybridization process—and goes throu

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Feature Selection Applied to Microarray Data,usually less than 100. In the context of microarray classification, this poses a challenge for machine learning methods, which can suffer overfitting and thus degradation in their performance. A common solution is to apply a dimensionality reduction technique before classification, to reduce the num

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Cluster Analysis of Microarray Data,phenomena have revived a great interest in cluster analysis due in part to the large amount of microarray data. Traditional clustering algorithms show, apart from the need of user-defined parameters, clear limitations to handle microarray data owing to its inherent characteristics: high-dimensional-

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ROC Curves for the Statistical Analysis of Microarray Data,s discrimination threshold. This chapter is an overview on the use of ROC curves for microarray data. The notion of ROC curve and its motivation is introduced in Subheading 1. Relevant scientific contributions concerning the use of ROC curves for microarray data are briefly reviewed in Subheading 2.

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Computer Tools to Analyze Microarray Data,single experiment. Different kinds of microarrays are available which are identifiable by characteristics such as the type of probes, the surface used as support, and the method used for target detection. Although microarrays have been applied in many biological areas, their management, and investig