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Author: search.filters.author.Kimpan, WarangkhanaSubject: search.filters.subject.Particle swarm optimization

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    Enhancing of Particle Swarm Optimization Based Method for Multiple Motifs Detection in DNA Sequences Collections
    (2020-05-01)
    Som-In, Sarawoot
    ;
    Kimpan, Warangkhana
    Genome sequence data consists of DNA sequences or input sequences. Each one includes nucleotides with chemical structures presented as characters: 'A ' 'C'' G''A','C','G', and 'T', and groups of motif sequences, called Transcription Factor Binding Sites (TFBSs), which are subsequences of DNA that lead to protein-synthesis. The detection of TFBSs is an important problem for bioinformatics research. With the similar patterns of motif sequences in TFBSs, computational algorithms for TFBSs detection have been improved to reduce resources used in laboratory setting. The metaheuristic algorithm is the important issue that has been continually improved to detect TFBSs with greater precision and recall. This paper proposes PSO_HD by applying Particle Swarm Optimization (PSO) as a pre-process and using Hamming distance to improve the efficiency of detecting TFBSs with more precision and recall. In order to measure its efficiency, the paper compares the TFBSs detection using PSO_HD algorithm with relevant algorithms in eight datasets. F-score is used as a measurement unit and compared to the related algorithms. The experimental results show that PSO_HD algorithm gives the highest average F-score, which can be indicated that the PSO_HD algorithm can improve the efficiency of detecting TFBSs with more precision and recall.
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    NexusPSO: A novel algorithm to detect transcription factor binding sites
    (2018-08-28)
    Som, Sarawoot
    ;
    Kimpan, Warangkhana
    The detection of transcription factor binding sites is a major problem in research in Biology. Methods and computer algorithms can be applied to reduce time complexity and cost of detecting transcription factor binding sites in laboratory experiments. One of the well-known methods commonly used is swarm intelligence. However, errors in detection of transcription factor binding sites can be caused by different binding sites in the same genome sequence. The purpose of this research is to improve the effectiveness and accuracy in the detection of transcription factor binding sites by applying the newly developed pre-processing procedure, Nexus, to Particle Swarm Optimization algorithm (NexusPSO). The accuracy of the NexusPSO algorithm was measured in comparison with other algorithms, using information content (IC) as an indicator, with Escherichia coli data. This study found that NexusPSO is the most accurate method being tested. NexusPSO was then tested using consensus sequences on Saccharomyces cerevisiae and Homo sapiens. NexusPSO showed nearly identical results when compared to DNA footprinting methods.