Bayesian Model Averaging for Biomarker Discovery From Genome-Wide Microarray Data
“A Practical Guide to Bioinformatics” begins with discussing how to manage the data archives securely (Chapter 1). Data is one of the major and necessary components for bioinformatics analysis. This chapter gives us an overview of how we can protect our data. Chapter 2 to Chapter 4 discusses what we can do on top of microarray data. Besides microarray data analysis, network analysis is another very important research area. Chapter 5 to Chapter 7 extends the use of microarray data and focuses on network analysis in three different topics, namely, gene regulatory network, fuzzy gene network and metabolic network. Apart from analysis, annotating and browsing gene sequences is an area that we should never ignore. Chapter 8 to Chapter 9 discusses this important area. The rest of the book discusses some interesting and emerging topics in bioinformatics, which includes meiotic recombination hotspots, primer and probe design for genome-wide DNA, single nucleotide polymorphism analysis, gene transcription regulation, and protein-protein interaction network comparison.
A Practical Guide to Bioinformatics Analysis
Yeung, Ka Yee, "Bayesian Model Averaging for Biomarker Discovery From Genome-Wide Microarray Data" (2010). School of Engineering and Technology Publications. 296.