A novel approach to detect copy number variation using segmentation and genetic algorithm

Authors:
Chihyun Park;Youngmi Yoon;Jaegyoon Ahn;Myungjin Moon;Sanghyun Park
Affiliations:
Yonsei University, South Korea;Yonsei University, South Korea and Gachon University of Medicine and Science, South Korea;Yonsei University, South Korea;Yonsei University, South Korea;Yonsei University, South Korea
Venue:
Proceedings of the 2009 ACM symposium on Applied Computing
Year:
2009

Citing 5
Cited 0

An Introduction to Genetic Algorithms

An Introduction to Genetic Algorithms
Introduction to Evolutionary Computing

Introduction to Evolutionary Computing
Hidden Markov models approach to the analysis of array CGH data

Journal of Multivariate Analysis
Biclustering of Expression Data with Evolutionary Computation

IEEE Transactions on Knowledge and Data Engineering
Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Among many forms of genomic variations, copy-number variations (CNVs) can be defined as gains or losses of several kilobases to hundreds of kilobases of genomic DNA. Since many CNVs include genes that result in differential levels of gene expression, CNVs may account for a significant proportion of normal phenotypic variation. Some scientists demonstrated that a large portion of overlapping, currently known common human CNVs, were smaller in his dataset. However, previous experimental studies, performed primarily by a-CGH techniques, are limited to detection of CNVs of large-sized CNVs. Efficient algorithms for finding small-sized CNVs are essential. In our paper, we propose a novel approach to find small-sized CNVs on a-CGH data which is a sequential 2-dimensional clustering method. The algorithm we propose is robust to some level of noise. And regardless of the size of probes, our algorithm can find CNVs consisting of small number of probes.