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

Argiris Sakellariou

ISBN 10: 3659944505 / ISBN 13: 9783659944505

Published by LAP LAMBERT Academic Publishing, 2016

About this Item

Computational Methods for Identifying Statistically Significant Genes | Applications to Gene Expression Data of Various Human Diseases | Argiris Sakellariou | Taschenbuch | 136 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659944505 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 107570380

Bibliographic Details

• Title: Computational Methods for Identifying Statistically Significant Genes | Applications to Gene Expression Data of Various Human Diseases
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2016
• Language: English
• ISBN 10: 3659944505
• ISBN 13: 9783659944505
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 221 grams
• Seller catalogs:
  • Bücher

About this title

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 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.