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Access type: UnknownSubject: search.filters.subject.Flower pollination algorithm

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    Efficient distributed SNP selection by a Modified Binary Flower Pollination Algorithm
    (2020-07-01)
    Rathasamuth, Wanthanee
    ;
    Pasupa, Kitsuchart
    Porcine Single Nucleotide Polymorphisms (SNPs-certain pieces of nucleotide in a DNA sequence) can be indirectly associated with traits of an individual pig, like its meat quality or resistance to common diseases. It is most desirable to obtain a smallest number of most significant SNPs in genomics research, and several computer classification algorithms have been used to do so. For instance, for breed classification, one needs to obtain a set of a much smaller number of significant SNPs than that of the entire SNP data set. This study attempted to find such significant porcine SNPs by using computational feature selection and classification methods. In a preliminary trial, a binary flower pollination algorithm (BFPA) was used and shown not to able to reduce the number of selected SNPs to a sufficiently low number. Therefore, to achieve our objective, we developed a vertically distributed feature selection method incorporating a modified BFPA and a support vector machine classifier for selecting significant porcine SNPs. The developed method was evaluated and compared against four baseline methods. It provided the smallest average number of significant SNPs (128.40) that resulted in 94.57% classification accuracy. This and other findings in this study may directly benefit researchers in the bioinformatics field in their effort to map SNPs.
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    A Modified Binary Flower Pollination Algorithm: A Fast and Effective Combination of Feature Selection Techniques for SNP Classification
    (2019-10-01)
    Rathasamuth, Wanthanee
    ;
    Pasupa, Kitsuchart
    Single nucleotide polymorphism (SNP) is a genetic trait responsible for the differences in the characteristics of individuals of a living species. Machine learning has been brought in to classify swine breed according to their SNPs. However, since the number of samples (number of pigs sampled) is usually much smaller than the number of features (SNPs) to classify, there may occur an overfitting problem. Therefore, some feature selection techniques were applied to the entire SNPs to reduce them to a much smaller number of most significant SNPs to be used in the classification. In this study, we used information gain in combination with binary flower pollination algorithm for feature selection as well as a cut-off-point-finding threshold for specifying a 0 or 1 value for a position in the solution vector and a GA bit-flip mutation operator. We called it Modified-BFPA. The classifier was SVM. Evaluated against a few other feature selection techniques, our combination of techniques was, at the very least, competitive to those. It selected only 1.76 % of most significant SNPs from the entire set of 10,210 SNPs. The SNPs that it selected provided 95.12 % classification accuracy. Moreover, it was fast: an average of 1.60 iterations in combination with SVM to find a set of best SNPs that provided the highest classification accuracy.