Computational Methods for Identifying Statistically Significant Genes: Applications to Gene Expression Data of Various Human Diseases - Softcover

Sakellariou, Argiris

Softcover

ISBN 10: 3659944505 ISBN 13: 9783659944505

Publisher: LAP LAMBERT Academic Publishing, 2016

Synopsis

The advent of microarray technology has revolutionized our knowledge about the underlying mechanisms of human diseases, based on the simultaneous hybridization of thousands of genes. Τhe analysis and mining in this immense amount of information necessitate the development of sophisticated algorithms and effective computational tools. The holy grail of those tools is to discern those maybe tens of genes among tens of thousands of genes that appear to differentiate their expression values systematically between two specific phenotypes. This endeavor is impeded by several factors including the inherent “noise” from the microarray technology and the poly-parametric nature of the diseases, which disguise the hunted patterns of differential gene expression. In this book, we propose a new hybrid feature selection method (mAP-KL) based on the hypothesis that among the statistically significant ranked genes in a gene list, there should be clusters of genes that share similar biological functions related to the investigated disease. Thus, instead of keeping N top ranked genes, it would be more appropriate to define and keep a number of gene cluster exemplars.

About this title

About the Author

Argiris D. Sakellariou holds a PhD in Bioinformatics from National & Kapodistrian University of Athens, M.Sc in Information Systems Engineering from The University of Manchester, and B.S degree in the field of Industrial Informatics from the Technological Educational Institute Of Piraeus. He carries out research on the field of Microarray Analysis.

Edition details

• Publisher: LAP LAMBERT Academic Publishing
• Publication date: 2016
• Language: English
• ISBN 10: 3659944505
• ISBN 13: 9783659944505
• Binding: Paperback
• Number of pages: 136