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Gene Expression Data Analysis

 Gene Expression Data  Analysis

Gene Expression Data  Analysis


Bioinformatics is an exciting field of scientific and technological innovation. This evolving field integrates several disciplines, including computer scienceand informatics, modern biology, statistics, applied mathematics and artificial intelligence to provide solutions to crucial biological problems at the molecular level. With the rapid growth in data capture and machine learning
techniques, it has become possible to obtain, organize, analyze and interpret biological data with an eye to uncovering hidden, non-trivial, and interesting patterns of valuable consequence.

One major area within bioinformatics is analysis of gene expression data of disparate kinds such as microarrays, RNAseq, gene ontologies, protein-protein interactions, and various flavors of genome sequence data or combinations. A major advantage of gene expression data produced from the microarray and sequencing technologies is that they provide dynamic information about cell function, whereas the genome provides only static information.

The measurement of the activity (expression) of thousands of genes at once so as to create a global picture of cellular function is known as gene expression profiling. Analysis and interpretation of such gene expression data using machine learning or statistical methods can help extract intrinsic patterns or knowledge, which may be of use towards uncovering causes of critical diseases.

Unlike most other computational biology books, this book focuses on gene expression data generation, characteristics of such data, and preprocessing to handle noise and redundancy, to help improve performance of analysis algorithms. This book will help readers learn about the various types of gene expression data such as microarray, MiRNA, RNAseq and ScRNA data in detail enabling them to develop data-centric algorithms towards interesting biological problem solving.


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