TY - JOUR
T1 - Prediction of transcription start sites based on feature selection using AMOSA.
AU - Wang, X.
AU - Bandyopadhyay, Sanghamitra
AU - Xuan, Zhenyu
AU - Zhao, Xiaoyue
AU - Zhang, Michael Q.
AU - Zhang, Xuegong
PY - 2007
Y1 - 2007
N2 - To understand the regulation of the gene expression, the identification of transcription start sites (TSSs) is a primary and important step. With the aim to improve the computational prediction accuracy, we focus on the most challenging task, i.e., to identify the TSSs within 50 bp in non-CpG related promoter regions. Due to the diversity of non-CpG related promoters, a large number of features are extracted. Effective feature selection can minimize the noise, improve the prediction accuracy, and also to discover biologically meaningful intrinsic properties. In this paper, a newly proposed multi-objective simulated annealing based optimization method, Archive Multi-Objective Simulated Annealing (AMOSA), is integrated with Linear Discriminant Analysis (LDA) to yield a combined feature selection and classification system. This system is found to be comparable to, often better than, several existing methods in terms of different quantitative performance measures.
AB - To understand the regulation of the gene expression, the identification of transcription start sites (TSSs) is a primary and important step. With the aim to improve the computational prediction accuracy, we focus on the most challenging task, i.e., to identify the TSSs within 50 bp in non-CpG related promoter regions. Due to the diversity of non-CpG related promoters, a large number of features are extracted. Effective feature selection can minimize the noise, improve the prediction accuracy, and also to discover biologically meaningful intrinsic properties. In this paper, a newly proposed multi-objective simulated annealing based optimization method, Archive Multi-Objective Simulated Annealing (AMOSA), is integrated with Linear Discriminant Analysis (LDA) to yield a combined feature selection and classification system. This system is found to be comparable to, often better than, several existing methods in terms of different quantitative performance measures.
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U2 - 10.1142/9781860948732_0021
DO - 10.1142/9781860948732_0021
M3 - Article
C2 - 17951823
AN - SCOPUS:38449092401
SN - 1752-7791
VL - 6
SP - 183
EP - 193
JO - Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference
JF - Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference
ER -