A CpGCluster-Teaching–Learning-Based Optimization for Prediction of CpG Islands in the Human Genome

Journal of computational biology: A journal of computational molecular cell biology

Li-YehChuang^3¹  Cheng-HongYang^1,2²  Yi-ChengChiang^1³ 

[1] Department of Chemical Engineering, Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan^3;Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan^1;Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan^2

关键词: CpG island detection;    clustering technology;    sliding window method;    teaching–learning-based optimization;   

DOI  :  10.1089/cmb.2016.0178

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Many CpG island detection methods have been proposed based on sliding window and clustering technology, but the accuracy of these methods is proportional to the time required. Therefore, an accurate and rapid method for identifying CpG islands remains an important challenge in the complete human genome. We propose a hybrid method CpGTLBO to detect the CpG islands in the human genome. The method uses the clustering approach and the teaching–learning-based optimization (TLBO) algorithm. The clustering approach is used to detect CpG island candidates, and it can effectively reduce the huge volume of unnecessary DNA fragments. TLBO was used to accurately predict CpG islands among promising CpG island candidates. A comparison based on six contig data sets and a whole human genome analysis showed that the identifying stability of CpGTLBO outperformed eight existing methods in terms of sensitivity (SN), specificity (SP), accuracy (ACC), performance coefficient (PC), and correlation coefficient (CC) and processing time. Results indicated that ClusterTLBO can effectively overcome the drawbacks and maintain the advantages in both the CpGcluster and TLBO.

【 授权许可】

Unknown