Efficient techniques for genotype‐phenotype correlational analysis

BMC Medical Informatics and Decision Making

Research Article

Jinbo Bi¹  Sudipta Pathak¹  Subrata Saha¹  Sanguthevar Rajasekaran¹ 

[1]Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, USA

关键词: Feature Selection Algorithm (FSA);    Gene Selection Algorithm (GSA);    Multifactor Dimensionality Reduction (MDR);    Random Projection (RP);    Single‐Nucleotide Polymorphism (SNP);    Support Vector Machine (SVM);    Principal Component Analysis (PCA);   

DOI  :  10.1186/1472-6947-13-41

 received in 2013-02-14, accepted in 2013-03-19,  发布年份 2013

来源: Springer

【 摘 要 】

BackgroundSingle Nucleotide Polymorphisms (SNPs) are sequence variations found in individuals at some specific points in the genomic sequence. As SNPs are highly conserved throughout evolution and within a population, the map of SNPs serves as an excellent genotypic marker. Conventional SNPs analysis mechanisms suffer from large run times, inefficient memory usage, and frequent overestimation. In this paper, we propose efficient, scalable, and reliable algorithms to select a small subset of SNPs from a large set of SNPs which can together be employed to perform phenotypic classification.MethodsOur algorithms exploit the techniques of gene selection and random projections to identify a meaningful subset of SNPs. To the best of our knowledge, these techniques have not been employed before in the context of genotype‐phenotype correlations. Random projections are used to project the input data into a lower dimensional space (closely preserving distances). Gene selection is then applied on the projected data to identify a subset of the most relevant SNPs.ResultsWe have compared the performance of our algorithms with one of the currently known best algorithms called Multifactor Dimensionality Reduction (MDR), and Principal Component Analysis (PCA) technique. Experimental results demonstrate that our algorithms are superior in terms of accuracy as well as run time.ConclusionsIn our proposed techniques, random projection is used to map data from a high dimensional space to a lower dimensional space, and thus overcomes the curse of dimensionality problem. From this space of reduced dimension, we select the best subset of attributes. It is a unique mechanism in the domain of SNPs analysis, and to the best of our knowledge it is not employed before. As revealed by our experimental results, our proposed techniques offer the potential of high accuracies while keeping the run times low.

【 授权许可】

Unknown   
© Saha et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.