Argiris Sakellariou (10 results)

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Paperback. Condition: Brand New. 136 pages. 8.66x5.91x0.31 inches. In Stock.

Taschenbuch. Condition: Neu. 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.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. he 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. 136 pp. Englisch.

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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sakellariou ArgirisArgiris 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 Indu.

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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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. ¿he 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 136 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. he 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.