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

Sakellariou, Argiris

 
9783659944505: Computational Methods for Identifying Statistically Significant Genes: Applications to Gene Expression Data of Various Human Diseases
Couverture souple
ISBN 10 : 3659944505 ISBN 13 : 9783659944505
Editeur : 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.

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Présentation de l'éditeur

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.

Biographie de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2016
Langue
anglais
ISBN 10 
3659944505
ISBN 13 
9783659944505
Reliure
Broché
Nombre de pages
136
Coordonnées du fabricant
non disponible
Personne responsable
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