DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.63
Cheng-Hong Yang , National Kaohsiung University of Applied Sciences, Kaohsiung
Jui-Hung Tsai , National Kaohsiung University of Applied Sciences, Kaohsiung
Cheng-Huei Yang , National Kaohsiung Institute of Marine Technology, Kaohsiung
Li-Yeh Chuang , I-Shou University, Kaohsiung
Operons contain valuable information for drug design and determining protein functions. Genes within an operon are co-transcribed to a single-strand mRNA and must be co-regulated. The identification of operons is thus critical for a detailed understanding of the gene regulations. However, currently used experimental methods for operon detection are generally difficult to implement and time-consuming. In this paper, we propose a chaotic binary particle swarm optimization (CBPSO) to predict operons in bacterial genomes. The intergenic distance, participation in the same metabolic pathway and the cluster of orthologous groups (COG) properties of the Escherichia coli genome are used to design a fitness function. Furthermore, the Bacillus subtilis, Pseudomonas aeruginosa PA01, Staphylococcus aureus and Mycobacterium tuberculosis genomes are tested and evaluated for accuracy, sensitivity, and specificity. The computational results indicate that the proposed method works effectively in terms of enhancing the performance of the operon prediction. The proposed method also achieved a good balance between sensitivity and specificity when compared to methods from the literature.
Pattern Recognition, Computer Applications, Life and Medical Sciences, Biology and genetics, Computing Methodologies
Cheng-Hong Yang, Jui-Hung Tsai, Cheng-Huei Yang, Li-Yeh Chuang, "Operon Prediction using Chaos Embedded Particle Swarm Optimization", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. , no. , pp. 0, 5555, doi:10.1109/TCBB.2013.63