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ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

Classifying Y-Short Tandem Repeat Data: A Decision Tree Approach

Ali Seman1*, Ida Rosmini Othman2, Azizian Mohd Sapawi1 and Zainab Abu Bakar1

1Center for Computer Science Studies, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia

2Center for Statistical Studies, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia

*Corresponding Author:
Ali Seman
Center for Computer Sciences
Faculty of Computer and Mathematical Sciences
Universiti Teknologi MARA (UiTM)
40450 Shah Alam, Selangor, Malaysia
Tel: +60355211191
Fax: +60355435100
E-mail: [email protected]

Received date: October 13, 2013; Accepted date: November 14, 2013; Published date: November 18, 2013

Citation: Seman A, Othman IR, Sapawi AM, Bakar ZA (2013) Classifying Y-Short Tandem Repeat Data: A Decision Tree Approach. J Proteomics Bioinform 6: 271-274. doi: 10.4172/jpb.1000290

Copyright: © 2013 Seman A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Classifying Y-Short Tandem Repeat data has recently been introduced in supervised and unsupervised classifications. This study continues the efforts in classifying YSTR data based on four decision tree models: CHisquared Automatic Interaction Detection (CHAID), Classification and Regression Tree (CART), Quick, Unbiased, Efficient Statistical Tree (QUEST) and C5. A data mining tool, called IBM Statistical Package for the Science Social Modeler 15.0 (IBM® SPSS® Modeler 15) was used for evaluating the performances of the models over six Y-STR data. Overall results showed that the decision tree models were able to classify all six Y-STR data significantly. Among the four models, C5 is the most consistent modelm where it had produced the highest accuracy score of 91.85%, sensitivity score of 93.69% and specificity score of 96.32%.

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