Datamining for Protein Sequence Analysis

Language: English

Published by LAP LAMBERT Academic Publishing Jan 2017, 2017

3330005068 / 9783330005068

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This item is printed on demand - Print on Demand Titel. Neuware -Data Mining of protein sequence databases poses challenges because many protein sequences are non-relational, whereas most Data Mining algorithms assume the input data to be relational database. Further, raw protein sequence database does not provide meaningful information until it is segregated into meaningful category. In this book, 1700 VEGF (Vascular Endothelial Growth Factor) Protein sequence dataset have been used and Data Mining algorithms are used for prediction. In Biocomputing, Data Mining (DM) techniques are widely used for prediction of protein structure. Interpreting voluminous Biological data is complex and the need for Data Mining concepts is significant. Molecular data such as DNA/Protein sequence, level of genetic expression, biochemical pathways, biomarkers and protein structures constitute a major part of biological data. The book discusses how standard Data Mining techniques such as extraction of protein data, segregation by clustering, association and visualization on a real time protein sequence dataset are performed. The existing integrated tool BioParisodhana is compared with BioBCDM where the novel tool outperforms BioParisodhana.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 168 pp. Englisch.

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Title
Datamining for Protein Sequence Analysis
Author
R. Deepa Lakshmi
Publisher
LAP LAMBERT Academic Publishing Jan 2017
Publication year
2017
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3330005068
ISBN 13
9783330005068
Item weight
268 grams
Dimensions
220x150x11 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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