Pasupa, Kitsuchart
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Pasupa, Kitsuchart
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kitsuchart.pa@kmitl.ac.th
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Item type:Publication, Selection of SNP Subsets for Severity of Beta-thalassaemia Classification Problem(2018-12-10); Tongsima, SissadesSingle-nucleotide polymorphisms (SNPs) are important genetic variables that are very popular in Genome-wide association study at the present time. They are often used in studies related to genetic disorders. A distinctive trait of SNPs is that there are a lot of them since they are variables originated from various positions in a DNA sequence. Unfortunately, the number of samples investigated are usually far fewer than the number of SNPs and so an over-fitting often occurs when one wants to construct a predictive model for classifying a sample into a case or a control. This study investigated a dataset on beta-thalassemia, a common genetic disorder widely found in Thai population. The data in the set are divided into two groups: severe and mild groups. The aims of the study were to develop and evaluate methods for screening and ranking SNPs related to this disorder. The screening methods tested were Chi-squared test (χ <sup>2</sup> ), Information Gain, and Gradient Boosting (GB). The SNPs that were screened in and selected were then used to construct a predictive model for classifying a sample to be either a severe or mild case. The model construction methods tested were Support Vector Machine (SVM), GB, and Naïve Bayes. Several combinations of a screening method and a model construction method were evaluated, and the evaluation results show that the best combination was χ <sup>2</sup> -SVM which used the number of selected SNPs of 10. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Counting and classification of malarial parasite from giemsa-stained thin film images(2020-01-01); ; Tongsima, SissadesMalaria is a life-threatening disease causing by an infection of the protozoan parasite Plasmodium. Plasmodium falciparum is the deadliest and most common human infected parasites hosted by anopheles mosquito vector. To cure a malaria infected patient and prevent further spreading, malaria diagnosis using microscopy to visualize Giemsa-stained parasites is commonly done. The microscopy diagnosis is somewhat time consuming and requires well-trained malaria experts to interpret what they see under the microscope. To address this limitation, an automated malaria infected diagnosis is needed. This work proposed a computer-aided automated diagnosis system that can perform remote field diagnosis with high accuracy while requiring less computational demands. The proposed framework consists of two main parts that are red blood cell counting and parasite life-cycle stage classification. The counting process is performed by computer vision techniques, namely Hough transform. Different machine learning techniques, i.e., Multilayer Perceptron, Linear Discriminant Analysis, Support Vector Machine, and Weighted Similarity Extreme Learning Machine, are employed in the classification task. We also demonstrated that combining hand-crafted and deep-learned features can enhance the overall performance of the framework. The experimental results showed that the proposed methods could correctly count and classify at 97.94% and 98.12% accuracy, respectively. The overall proposal system can achieve at 96.18% accuracy. This is achieved by WELM in conjunction with deep-learned (AlexNet_FC7) and the hand-crafted (color) features. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discovery of significant porcine SNPs for swine breed identification by a hybrid of information gain, genetic algorithm, and frequency feature selection technique(2020-05-26); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Selection of a minimal number of significant porcine snps by an information gain and genetic algorithm hybrid model(2019-01-01); Tongsima, SissadesA panel of a large number of common Single Nucleotide Polymorphisms (SNPs) distributed across an entire porcine genome has been widely used to represent genetic variability of pigs. With the advent of SNP-array technology, a genome-wide genetic profile of a specimen can be easily observed. Among the large number of such variations, there exists a much smaller subset of the SNP panel that could equally be used to correctly identify the corresponding breed. This work presents a SNP selection heuristic that can still be used effectively in the breed classification. The features were selected by combining a filter method and a wrapper method-information gain method and genetic algorithm-plus a feature frequency selection step, while classification used a support vector machine. We were able to reduce the number of significant SNPs to 0.86 % of the total number of SNPs in a swine dataset with 94.80 % classification accuracy.