Main description:

This book provides a comprehensive, interdisciplinary collection of the main, up-to-date methods, tools, and techniques for microarray data analysis, covering the necessary steps for the acquisition of the data, its preprocessing, and its posterior analysis. Featuring perspectives from biology, computer science, and statistics, the volume explores machine learning methods such as clustering, feature selection, classification, data normalization, and missing value imputation, as well as the statistical analysis of the data and the most popular computer tools to analyze microarray data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will aid researchers in getting successful results.
Cutting-edge and authoritative, Microarray Bioinformatics serves as an ideal guide for researchers and graduate students in bioinformatics, with basic knowledge in biology and computer science, and with a view to work with microarray datasets.

Contents:

Introduction to Bioinformatics
Introduction to DNA microarrays
Data Warehousing
Microarray Annotation
A review of microarray datasets: where to find them and specific characteristics
Statistical analysis of microarray data
Feature selection applied to microarray data
Cluster analysis of microarray data
Classification of microarray data
Microarray data normalization and transformation
HPC tools to deal with microarray data
Sequence alignment with microarray data
Experimental design of microarrays
ROC curves for microarrays
Image processing and microarrays
Challenges and future trends