Ebook: Microarray analysis : biochips and eradication of all diseases
Author: Sharma Kal Renganathan
- Tags: DNA microarrays. SCIENCE / Life Sciences / Biochemistry Genominomics Next Generation Sequencing Microarray Analysis Cancer Treatment Cancer Progression Gene Silencing Single Nucleotide Polymorphism Genetic Disorders Autoimmune Disorders Genetically Modified Crops Small Pox
- Series: Biomedical engineering collection
- Year: 2015
- Publisher: Momentum Press
- Edition: 1
- Language: English
- pdf
Microarray Analysis will be developed as a natural outgrowth of the authors teaching and research into the field of Microarray processing, a fast growing and developing field. The book will discuss the development of various microarray analysis tools and the applications of the technology towards disease prevention and eradication. The goal of the book is to illustrate the many sequencing methods in place, and to show the various applications of these methods. This book will show how the biochemical reactions involved in the different steps of the cure process can be studied using microarray methods. Examples will be provided throughout the text of various disease states, including the spread of cystic fibrosis in children from consanguineous marriages in Saudi Arabia, genetic disorders and, and autoimmune disorders. The protocols from sample preparation to confocal scanning microscopy and detectors using photo multiplier tubes will be discussed in detail. The Polymerase chain reaction, a technique used to amplify and simultaneously quantify a targeted DNA molecule will be explored in detail in relation to the best surface to use for Microarray Analysis. Additionally, Microarray Analysis: Biochips and Eradication of Disease will feature the statistical methods needed for analysis of DNA data from microarray analysis, including shotgun and next generation sequencing. The applications of these methods including data normalization, cluster analysis and predictions from a microarray data set, will be examined in detail. Finally, the statistical design of experiments, computing methods for linear and nonlinear regression, robust estimation and general maximum-likelihood methods will be explained for use in future experiments
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