Datamining for Protein Sequence Analysis

R. Deepa Lakshmi

ISBN 10: 3330005068 / ISBN 13: 9783330005068

Published by LAP LAMBERT Academic Publishing, 2017

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9783330005068

Bibliographic Details

• Title: Datamining for Protein Sequence Analysis
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 3330005068
• ISBN 13: 9783330005068
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 11D millimeters 220 millimeters width by 150 millimeters height by 11 millimeters depth
• Weight: 268 grams

About this title

Synopsis

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.

About the Author

Authors Special interests in research include Bioinformatics, Data Mining and they Published articles with high Impact Factor and Scopus indexed. This book will be a motivation to the blooming researchers in Bioinformatics and Data mining. It will definitely quench the thirst of computer science researchers interested in bioinformatics.