Prediction of transcription start sites based on feature selection using AMOSA.

X. Wang, Sanghamitra Bandyopadhyay, Zhenyu Xuan, Xiaoyue Zhao, Michael Q. Zhang, Xuegong Zhang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)183-193
Number of pages11
JournalComputational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference
StatePublished - 2007
Externally publishedYes

ASJC Scopus subject areas

  • General Medicine


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