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

Seman, Ali; Othman, Ida Rosmini; Sapawi, Azizian Mohd; Bakar, Zainab Abu

doi:10.4172/jpb.1000290

Cited by 1 publication

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“…However, where the sample size is large and/or there are multiple samples, multi-criteria analyses such as supervised and unsupervised learning methods produce results that are more informative. Indeed, several methods for grouping multiple samples of Y-STR data automatically have been reported (Schlecht et al, 2008 ; Seman et al, 2010a ; 2012 ; 2013a ). In the supervised learning method, Y-STR data can be classified by haplogroup via the decision tree method (Schlecht et al, 2008 ; Seman et al, 2013a ), Bayesian modeling, and support vector machines (Schlecht et al, 2008 ).…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, several methods for grouping multiple samples of Y-STR data automatically have been reported (Schlecht et al, 2008 ; Seman et al, 2010a ; 2012 ; 2013a ). In the supervised learning method, Y-STR data can be classified by haplogroup via the decision tree method (Schlecht et al, 2008 ; Seman et al, 2013a ), Bayesian modeling, and support vector machines (Schlecht et al, 2008 ). Similarly, unsupervised learning methods can be used to cluster Y-STR data by similar genetic distances (Seman et al, 2010a ; 2010b ; 2010c ; 2010d ; 2012 ).…”

Section: Introductionmentioning

confidence: 99%

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Towards Development of Clustering Applications for Large-Scale Comparative Genotyping and Kinship Analysis Using Y-Short Tandem Repeats

Seman

Sapawi

Salleh

2015

OMICS: A Journal of Integrative Biology

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Y-chromosome short tandem repeats (Y-STRs) are genetic markers with practical applications in human identification. However, where mass identification is required (e.g., in the aftermath of disasters with significant fatalities), the efficiency of the process could be improved with new statistical approaches. Clustering applications are relatively new tools for large-scale comparative genotyping, and the k-Approximate Modal Haplotype (k-AMH), an efficient algorithm for clustering large-scale Y-STR data, represents a promising method for developing these tools. In this study we improved the k-AMH and produced three new algorithms: the Nk-AMH I (including a new initial cluster center selection), the Nk-AMH II (including a new dominant weighting value), and the Nk-AMH III (combining I and II). The Nk-AMH III was the superior algorithm, with mean clustering accuracy that increased in four out of six datasets and remained at 100% in the other two. Additionally, the Nk-AMH III achieved a 2% higher overall mean clustering accuracy score than the k-AMH, as well as optimal accuracy for all datasets (0.84–1.00). With inclusion of the two new methods, the Nk-AMH III produced an optimal solution for clustering Y-STR data; thus, the algorithm has potential for further development towards fully automatic clustering of any large-scale genotypic data.

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