Skip to main content

Yu-da Lin

National Penghu University, Department of Computer Science and Information Engineering, Professor

Followers

2

Following

1

Public Views

Renju Zachariah

Pairach (Champ) Piboonrungroj

Chiang Mai University

Natasa Glisovic

Interests

Uploads

Papers by Yu-da Lin

Analysis and Forecasting of International Airport Traffic Volume

Mathematics

Globalization has resulted in increases in air transportation demand and air passenger traffic. W... more Globalization has resulted in increases in air transportation demand and air passenger traffic. With the increases in air traffic, airports face challenges related to infrastructure, air services, and future development. Air traffic forecasting is essential to ensuring appropriate investment in airports. In this study, we combined fuzzy theory with support vector regression (SVR) to develop a fuzzy SVR (FSVR) model for forecasting international airport traffic. This model was used to predict the air traffic volumes at the world’s 10 busiest airports in terms of air traffic in 2018. The predictions were made for the period from August 2014 to December 2019. For fuzzy time series, the developed FSVR model can consider historical air traffic changes. The FSVR model can suitably divide air traffic changes into appropriate fuzzy sets, generate membership function values, and establish fuzzy relations to produce fuzzy interpolated values with minimal errors. Thus, in the prediction of con...

Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes

Mathematics

As freight volumes increase, airports are likely to require additional infrastructure development... more As freight volumes increase, airports are likely to require additional infrastructure development, increased air services, and expanded facilities. Prediction of freight volumes could ensure effective investment. Among the computational intelligence models, support vector regression (SVR) has become the dominant modeling paradigm. In this study, a fuzzy-based SVR (FSVR) model was used to solve the freight volume prediction problem in international airports. The FSVR model can use a fuzzy time series of historical traffic changes for predictions. A fuzzy classification algorithm was used for elements of similar levels in the time series to appropriately divide traffic changes into fuzzy sets, generate membership function values, and establish a fuzzy relationship to produce a fuzzy interpolation with a minimal error. A comparison of the FSVR model with other models revealed that the FSVR model had the lowest mean absolute percentage error (all < 2.5%), mean absolute error, and roo...

Taguchi-enhanced binary differential evolution algorithm

2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2017

This paper proposed an improved binary differential evolution (BDE) algorithm based on a Taguchi ... more This paper proposed an improved binary differential evolution (BDE) algorithm based on a Taguchi approach (Taguchi-BDE) for optimization problems. An estimation operator with efficient probability was adopted to preserve the diversity of populations and enhance the global search ability in Taguchi-BDE, and the Taguchi approach was used to optimize solutions after the crossover operations in Taguchi-BDE The performance of the Taguchi-BDE algorithm was evaluated on a set of ten numerical benchmarks, and the results showed that our algorithm achieved better mean values over 40 repeated tests than did the other BDE-based algorithms. Taguchi-BDE was also applied to a real-world problem in statistical data mining, namely identification of interactions between single nucleotide polymorphisms. A total of 1,500 data sets under different settings were used to evaluate the efficiency of Taguchi-BDE, and the results showed that the proposed algorithm can achieve a 92.07% success rate. In conclusion, Taguchi-BDE revealed efficient convergence toward promising search regions, and achieved satisfactory search results.

Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider

Mathematics, 2021

Cost–benefit analysis is widely used to elucidate the association between foraging group size and... more Cost–benefit analysis is widely used to elucidate the association between foraging group size and resource size. Despite advances in the development of theoretical frameworks, however, the empirical systems used for testing are hindered by the vagaries of field surveys and incomplete data. This study developed the three approaches to data imputation based on machine learning (ML) algorithms with the aim of rescuing valuable field data. Using 163 host spider webs (132 complete data and 31 incomplete data), our results indicated that the data imputation based on random forest algorithm outperformed classification and regression trees, the k-nearest neighbor, and other conventional approaches (Wilcoxon signed-rank test and correlation difference have p-value from < 0.001–0.030). We then used rescued data based on a natural system involving kleptoparasitic spiders from Taiwan and Vietnam (Argyrodes miniaceus, Theridiidae) to test the occurrence and group size of kleptoparasites in na...

Improved Classification Method for Detecting Potential Interactions Between Genes

Advances in Intelligent Systems and Computing, 2018

Multifactor dimensionality reduction (MDR) constitutes a highly accurate classification algorithm... more Multifactor dimensionality reduction (MDR) constitutes a highly accurate classification algorithm for gene-gene interaction (GGI) identification. GGI detection quality is commonly assessed using the correct classification rate (CCR). Nevertheless, the CCR alone might not be suitable for assessing the detection of some GGIs due to various model preferences and disease complexities. Accordingly, we developed an MDR-based multiple-objective method that combines the CCR and chi-squared measures (called MDR-Cv 2) for GGI detection. In the proposed method, a Pareto set operation is executed to ensure the combination of the CCR and chi-squared measures in the MDR process for GGI detection. The most significant GGIs within the Pareto sets are determined using cross-validation consistency values. Herein, we report the MDR and MDR-Cv 2 detection success rates in a simulated environment and demonstrate that the proposed MDR-Cv 2 provides superior GGI detection success rates.

A Teaching-Learning-Based Optimization for Operon Prediction

Asian Journal of Engineering and Technology, 2014

Operons are the basic unit of transcription and can be used to understand the transcription regul... more Operons are the basic unit of transcription and can be used to understand the transcription regulation in a given prokaryotic genome. Currently, the sequence and gene coordinates of organisms can be rapidly identified, but their operons remain unknown. Moreover, the experimental methods detecting operons are extremely difficult and time-consuming to execute. Operon prediction as pretreatment can greatly reduce the cost of performing an experimental assay. Many algorithms and biological properties have been proposed but the resulting predictions still require improvement in terms of sensitivity, specificity, and accuracy. This study uses a teaching-learning-based optimization (TLBO) algorithm with three biological properties for operon prediction: the intergenic distance, the metabolic pathway, and the cluster of orthologous groups (COG). These properties for the Escherichia coli genome are used to train the evaluation standards of fitness function of gene pairs. The experimental res...

Sinusoidal Map Based Particle Swarm Optimization Detect the SNP Barcode in Breast Cancer to Disease Susceptibility

Asian Journal of Engineering and Technology, 2014

Single nucleotide polymorphisms (SNPs) are the most common type of DNA sequence variation in the ... more Single nucleotide polymorphisms (SNPs) are the most common type of DNA sequence variation in the human genome and are widely used to investigate the association analysis of diseases. SNP barcode is a combination of SNPs with genotypes (AA, Aa, and aa for an SNP) to find the difference between case data set and control data set for analyzing the disease association amongst SNPs. Currently, the computational time of statistical method becomes the weak to analyze the big data to find the significant SNP barcode. Here, we applied a sinusoidal particle swarm optimization (SPSO) algorithm facilitate the statistical methods to analyze the associated SNPs. We systematically evaluated the synergistic effect of 26 SNPs from eight epigenetic modifier-related genes in breast cancer. The 2- to 5-order SNP barcodes were found to determine the risk effects in breast cancer. We found that five of eight genes (BAT8, DNMT3A, EHMT1, DNMT3A, and BAT8) were statistically significant to breast cancer and...

An Improved Particle Swarm Optimization for Data Clustering

In recent years, clustering is still a popular analysis tool for data statistics. The data struct... more In recent years, clustering is still a popular analysis tool for data statistics. The data structure identifying from the large-scale data has become a very important issue in the data mining problem. In this paper, an improved particle swarm optimization based on Gauss chaotic map for clustering is proposed. Gauss chaotic map adopts a random sequence with a random starting point as a parameter, and relies on this parameter to update the positions and velocities of the particles. It provides the significant chaos distribution to balance the exploration and exploitation capability for search process. This easy and fast function generates a random seed processes, and further improve the performance of PSO due to their unpredictability. In the experiments, the eight different clustering algorithms were extensively compared on six test data. The results indicate that the performance of our proposed method is significantly better than the performance of other algorithms for data clusteri...

An Improved Particle Swarm Optimization to detect the SNP Barcode for Breast Cancer Prediction

The analyses of disease-associated polymorphisms by genome-wide association are important studies... more The analyses of disease-associated polymorphisms by genome-wide association are important studies for the detection of influences upon their interaction with other genetic factors. The gene can be examined in isolation without allowing potential interactions with other unknown factors, which might result in neglect of some disease influences. Single nucleotide polymorphism (SNP) barcodes can be applied for disease prediction and allow the detection of these influences. The particle swarm optimization (PSO) has been applied to detect the SNP barcode; however, PSO may fault to result in the non-significant SNP barcode. In this study, an improved PSO algorithm was introduced to detect the significant SNP barcodes for breast cancer prediction. A chaotic theory was applied to improve the PSO for finding the good SNP barcodes. The breast cancer datasets were simulated by using the genotype frequencies as described in the literature from steroid hormones and their signalling and metabolic ...

Effect of Baseline Characteristics and Tumor Burden on Vaspin Expression and Progressive Disease in Operable Colorectal Cancer

Diagnostics, 2020

Colorectal cancer is a highly heterogeneous malignancy in the Asian population, and it is conside... more Colorectal cancer is a highly heterogeneous malignancy in the Asian population, and it is considered an important prognostic factor for baseline characteristics, tumor burden, and tumor markers. This study investigated the effect of baseline characteristics and tumor burden on tumor marker expression and progressive disease in colorectal cancer by using partial least squares variance-based path modeling (PLS-PM). PLS-PM can be used to evaluate the complex relationship between prognostic variables and progressive disease status with a small sample of measurements and structural models. A total of 89 tissue samples of colorectal cancer were analyzed. Our results suggested that the expression of visceral adipose tissue-derived serpin (vaspin) is a potential indicator of colorectal cancer progression and may be affected by baseline characteristics such as age, sex, body mass index, and diabetes mellitus. Moreover, according to the characteristics of tumor burden, the expression of vaspi...

Epistasis Analysis Using an Improved Fuzzy C-Means-Based Entropy Approach

IEEE Transactions on Fuzzy Systems, 2019

Epistasis detection is vital to determining disease susceptibility in the human genome. With rapi... more Epistasis detection is vital to determining disease susceptibility in the human genome. With rapid advances in technology, multifactor dimensionality reduction (MDR) has become an effective algorithm for epistasis detection. Classification of high-risk (H) and low-risk (L) groups in MDR operations is a key topic but has not been thoroughly investigated. In this paper, we propose an improved fuzzy c-means-based entropy (FCME) approach to address the limitations of binary classification. For this approach, the degree of membership in MDR, referred to as FCMEMDR, was used. The FCME approach and MDR measure were integrated to enable more precise differentiation between similar frequencies of multifactor genotypes in cases of possible epistasis. We used the MDR measures of correct classification rate and likelihood ratio. Numerous simulated datasets were applied, and the experimental results revealed two measures of FCMEMDR with higher detection rates than those of other MDR-based algorithms. Our analysis of binary and fuzzy classifications in MDR operations may offer insights into the problem of uncertainty in H/L classification. Two measures of FCMEMDR detected significant instances of epistasis associated with coronary artery disease in the Wellcome Trust Case Control Consortium dataset. FCMEMDR is freely available at https://gitlab.com/yudalinemail/fcmemdr.

Detecting SNP–SNP Interactions in Imbalanced Case-Control Study

IEEE Access, 2019

SNP-SNP interactions are particularly informative biomarkers regarding the genetic components of ... more SNP-SNP interactions are particularly informative biomarkers regarding the genetic components of disease risk. However, SNP-SNP interaction identifications are yet limited in imbalanced case-control study. In this study, we proposed a multiobjective multifactor dimensionality reduction (MOMDR) based on three balancing approaches (BMOMDR), including (1) stratified K-fold crossvalidation; (2) balanced estimation of ratio between cases and controls; (3) balanced measures of SNP-SNP interactions, to effectively identify SNP-SNP interaction in imbalanced case-control study. BMOMDR was evaluated by extensive experiments on both simulated imbalanced case-control datasets and real genome-wide data from Wellcome Trust Case Control Consortium (WTCCC). For the simulated datasets, the results indicated that three balancing approaches can enhance the detection success rate of SNP-SNP interaction by MOMDR in imbalanced datasets. For WTCCC datasets, the results of SNP-SNP interaction detection obtained from BMOMDR revealed statistically significant (p <0.0001), revealing that BMOMDR can effectively identify SNP-SNP interaction in imbalanced case-control study. BMOMDR is freely available at http://shorturl.at/bluJS. INDEX TERMS SNP-SNP interactions, multiobjective approach multifactor dimensionality reduction, imbalanced case-control study.

Catfish Taguchi-Based Binary Differential Evolution Algorithm for Analyzing Single Nucleotide Polymorphism Interactions in Chronic Dialysis

IEEE Transactions on NanoBioscience, 2018

Single-nucleotide polymorphism (SNP)-SNP interactions are crucial for understanding the associati... more Single-nucleotide polymorphism (SNP)-SNP interactions are crucial for understanding the association between disease-related multifactorials for disease analysis. Existing statistical methods for determining such interactions are limited by the considerable computation required for evaluating all potential associations between disease-related multifactorials. Identifying SNP-SNP interactions is thus a major challenge in genetic association studies. This study proposes a catfish Taguchi-based binary differential evolution (CT-BDE) algorithm for identifying SNP-SNP interactions. In the search space, the catfish effect prevents the premature convergence of the population, and the Taguchi method improves the search ability of the BDE algorithm. Hence, the proposed algorithm enables obtaining a favorable solution regarding the identification of high-order SNP-SNP interactions. Additionally, the proposed algorithm applies an effective fitness function derived from a multifactor dimensionality reduction (MDR) operation to evaluate the solutions from BDE-based algorithms. Simulated and real data sets were used to evaluate the ability of several BDE-based algorithms in identifying specific SNP-SNP interactions. We compared the fitness function derived from the MDR operation with that derived according to the difference between cases and controls, by using the different BDE-based algorithms. The results showed that the proposed CT-BDE algorithm applying the fitness function derived from the MDR operation exhibited a superior ability in identifying SNP-SNP interactions compared with the other BDE-based algorithms.

Genetic Algorithm Combined with a Local Search Method for Identifying Susceptibility Genes

Journal of Artificial Intelligence and Soft Computing Research, 2016

Detecting genetic association models between single nucleotide polymorphisms (SNPs) in various di... more Detecting genetic association models between single nucleotide polymorphisms (SNPs) in various disease-related genes can help to understand susceptibility to disease. Statistical tools have been widely used to detect significant genetic association models, according to their related statistical values, including odds ratio (OR), chi-square test (χ2), p-value, etc. However, the high number of computations entailed in such operations may limit the capacity of such statistical tools to detect high-order genetic associations. In this study, we propose lsGA algorithm, a genetic algorithm based on local search method, to detect significant genetic association models amongst large numbers of SNP combinations. We used two disease models to simulate the large data sets considering the minor allele frequency (MAF), number of SNPs, and number of samples. The three-order epistasis models were evaluated by chi-square test (χ2) to evaluate the significance (P-value < 0.05). Analysis results sh...

A Hybrid Approach for CpG Island Detection in the Human Genome

PLOS ONE, 2016

Background CpG islands have been demonstrated to influence local chromatin structures and simplif... more Background CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging. Methodology/Principal Findings A novel procedure is proposed to detect CpG islands by combining clustering technology with the sliding-window method (PSO-based). Clustering technology is used to detect the locations of all possible CpG islands and process the data, thus effectively obviating the need for the extensive and unnecessary processing of DNA fragments, and thus improving the efficiency of sliding-window based particle swarm optimization (PSO) search. This proposed approach, named ClusterPSO, provides versatile and highly-sensitive detection of CpG islands in the human genome. In addition, the detection efficiency of ClusterPSO is compared with eight CpG island detection methods in the human genome. Comparison of the detection efficiency for the CpG islands in human genome, including sensitivity, specificity, accuracy, performance coefficient (PC), and correlation coefficient (CC), Clus-terPSO revealed superior detection ability among all of the test methods. Moreover, the combination of clustering technology and PSO method can successfully overcome their respective drawbacks while maintaining their advantages. Thus, clustering technology could be hybridized with the optimization algorithm method to optimize CpG island detection. Conclusion/Significance The prediction accuracy of ClusterPSO was quite high, indicating the combination of CpGcluster and PSO has several advantages over CpGcluster and PSO alone. In addition, ClusterPSO significantly reduced implementation time.

Determination of the SNP-SNP Interaction between Breast Cancer Related Genes to Analyze the Disease Susceptibility

International Journal of Machine Learning and Computing, 2014

Investigation of the single nucleotide polymorphism (SNP)-SNP interaction model can facilitate th... more Investigation of the single nucleotide polymorphism (SNP)-SNP interaction model can facilitate the analysis of the susceptibility to disease. The model explains the risk of association between the genotypes and the disease in case-control study. Thus, many mathematic methods are widely applied to identify the statistically significant model such as odds ratio (OR), chi-square test, and error rate. However, a huge number of data sets have been found to limit the statistical methods to identify the significant model. In this study, we propose a novel statistical method, complementary-logic particle swarm optimization (CLPSO), to increase the efficiency of significant model identification in case-control study. The complementary-logic is implemented to improve the PSO search ability and identify a better SNP-SNP interaction model. Six important breast cancer genes including 23 SNPs and simulated huge number of data sets were selected as the test data sets. The methods of PSO and CLPSO were applied on the identification of SNP-SNP interactions in the two-way to five-way. In results, the OR evaluates the breast cancer risk of the identified SNP-SNP interaction model. Compared to the corresponding non-interaction model, if the OR value is greater than 1 that indicates the model is significant risk between cases and controls. The results showed that CLPSO is able to identify the significant models for specific SNP-SNP interaction of two-way to five-way (OR value: 1.153-1.391; confidence interval (CI): 1.05-1.79; p-value: 0.01-0.003). The model suggests that the genes ESR1, PGR, and SHBG may be an important role in the interactive effects to breast cancer. In addition, we compared the search abilities of PSO and CLPSO for identification of the significant model. Results revealed that CLPSO can identify better model with difference values between cases and controls than PSO; it suggests CLPSO can be used to identify a better SNP-SNP interaction models. Index Terms-Single nucleotide polymorphism (SNP), particle swarm optimization (PSO), breast cancer. I. INTRODUCTION SNP is an common bio-marker in genomes, and it has widely used in the investigation of association analysis of diseases [1], cancers [2], and pharmacogenomics [3].

Limited-Parameter Optimization for PtRNASS using Chaotic Particle Swarm Optimization

Odds ratio based complementary-logic particle swarm optimization for multiple gene-gene interactions in breast cancer susceptibility

A Method of Predicting tRNA Secondary Structure from Nucleotide Sequences

PtRNASS: Prediction of tRNA Secondary Structure from Nucleotide Sequences

IAENG International Journal of Computer Science

Analysis and Forecasting of International Airport Traffic Volume

Mathematics

Globalization has resulted in increases in air transportation demand and air passenger traffic. W... more Globalization has resulted in increases in air transportation demand and air passenger traffic. With the increases in air traffic, airports face challenges related to infrastructure, air services, and future development. Air traffic forecasting is essential to ensuring appropriate investment in airports. In this study, we combined fuzzy theory with support vector regression (SVR) to develop a fuzzy SVR (FSVR) model for forecasting international airport traffic. This model was used to predict the air traffic volumes at the world’s 10 busiest airports in terms of air traffic in 2018. The predictions were made for the period from August 2014 to December 2019. For fuzzy time series, the developed FSVR model can consider historical air traffic changes. The FSVR model can suitably divide air traffic changes into appropriate fuzzy sets, generate membership function values, and establish fuzzy relations to produce fuzzy interpolated values with minimal errors. Thus, in the prediction of con...

Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes

Mathematics

As freight volumes increase, airports are likely to require additional infrastructure development... more As freight volumes increase, airports are likely to require additional infrastructure development, increased air services, and expanded facilities. Prediction of freight volumes could ensure effective investment. Among the computational intelligence models, support vector regression (SVR) has become the dominant modeling paradigm. In this study, a fuzzy-based SVR (FSVR) model was used to solve the freight volume prediction problem in international airports. The FSVR model can use a fuzzy time series of historical traffic changes for predictions. A fuzzy classification algorithm was used for elements of similar levels in the time series to appropriately divide traffic changes into fuzzy sets, generate membership function values, and establish a fuzzy relationship to produce a fuzzy interpolation with a minimal error. A comparison of the FSVR model with other models revealed that the FSVR model had the lowest mean absolute percentage error (all < 2.5%), mean absolute error, and roo...

Taguchi-enhanced binary differential evolution algorithm

2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2017

This paper proposed an improved binary differential evolution (BDE) algorithm based on a Taguchi ... more This paper proposed an improved binary differential evolution (BDE) algorithm based on a Taguchi approach (Taguchi-BDE) for optimization problems. An estimation operator with efficient probability was adopted to preserve the diversity of populations and enhance the global search ability in Taguchi-BDE, and the Taguchi approach was used to optimize solutions after the crossover operations in Taguchi-BDE The performance of the Taguchi-BDE algorithm was evaluated on a set of ten numerical benchmarks, and the results showed that our algorithm achieved better mean values over 40 repeated tests than did the other BDE-based algorithms. Taguchi-BDE was also applied to a real-world problem in statistical data mining, namely identification of interactions between single nucleotide polymorphisms. A total of 1,500 data sets under different settings were used to evaluate the efficiency of Taguchi-BDE, and the results showed that the proposed algorithm can achieve a 92.07% success rate. In conclusion, Taguchi-BDE revealed efficient convergence toward promising search regions, and achieved satisfactory search results.

Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider

Mathematics, 2021

Cost–benefit analysis is widely used to elucidate the association between foraging group size and... more Cost–benefit analysis is widely used to elucidate the association between foraging group size and resource size. Despite advances in the development of theoretical frameworks, however, the empirical systems used for testing are hindered by the vagaries of field surveys and incomplete data. This study developed the three approaches to data imputation based on machine learning (ML) algorithms with the aim of rescuing valuable field data. Using 163 host spider webs (132 complete data and 31 incomplete data), our results indicated that the data imputation based on random forest algorithm outperformed classification and regression trees, the k-nearest neighbor, and other conventional approaches (Wilcoxon signed-rank test and correlation difference have p-value from < 0.001–0.030). We then used rescued data based on a natural system involving kleptoparasitic spiders from Taiwan and Vietnam (Argyrodes miniaceus, Theridiidae) to test the occurrence and group size of kleptoparasites in na...

Improved Classification Method for Detecting Potential Interactions Between Genes

Advances in Intelligent Systems and Computing, 2018

Multifactor dimensionality reduction (MDR) constitutes a highly accurate classification algorithm... more Multifactor dimensionality reduction (MDR) constitutes a highly accurate classification algorithm for gene-gene interaction (GGI) identification. GGI detection quality is commonly assessed using the correct classification rate (CCR). Nevertheless, the CCR alone might not be suitable for assessing the detection of some GGIs due to various model preferences and disease complexities. Accordingly, we developed an MDR-based multiple-objective method that combines the CCR and chi-squared measures (called MDR-Cv 2) for GGI detection. In the proposed method, a Pareto set operation is executed to ensure the combination of the CCR and chi-squared measures in the MDR process for GGI detection. The most significant GGIs within the Pareto sets are determined using cross-validation consistency values. Herein, we report the MDR and MDR-Cv 2 detection success rates in a simulated environment and demonstrate that the proposed MDR-Cv 2 provides superior GGI detection success rates.

A Teaching-Learning-Based Optimization for Operon Prediction

Asian Journal of Engineering and Technology, 2014

Operons are the basic unit of transcription and can be used to understand the transcription regul... more Operons are the basic unit of transcription and can be used to understand the transcription regulation in a given prokaryotic genome. Currently, the sequence and gene coordinates of organisms can be rapidly identified, but their operons remain unknown. Moreover, the experimental methods detecting operons are extremely difficult and time-consuming to execute. Operon prediction as pretreatment can greatly reduce the cost of performing an experimental assay. Many algorithms and biological properties have been proposed but the resulting predictions still require improvement in terms of sensitivity, specificity, and accuracy. This study uses a teaching-learning-based optimization (TLBO) algorithm with three biological properties for operon prediction: the intergenic distance, the metabolic pathway, and the cluster of orthologous groups (COG). These properties for the Escherichia coli genome are used to train the evaluation standards of fitness function of gene pairs. The experimental res...

Sinusoidal Map Based Particle Swarm Optimization Detect the SNP Barcode in Breast Cancer to Disease Susceptibility

Asian Journal of Engineering and Technology, 2014

Single nucleotide polymorphisms (SNPs) are the most common type of DNA sequence variation in the ... more Single nucleotide polymorphisms (SNPs) are the most common type of DNA sequence variation in the human genome and are widely used to investigate the association analysis of diseases. SNP barcode is a combination of SNPs with genotypes (AA, Aa, and aa for an SNP) to find the difference between case data set and control data set for analyzing the disease association amongst SNPs. Currently, the computational time of statistical method becomes the weak to analyze the big data to find the significant SNP barcode. Here, we applied a sinusoidal particle swarm optimization (SPSO) algorithm facilitate the statistical methods to analyze the associated SNPs. We systematically evaluated the synergistic effect of 26 SNPs from eight epigenetic modifier-related genes in breast cancer. The 2- to 5-order SNP barcodes were found to determine the risk effects in breast cancer. We found that five of eight genes (BAT8, DNMT3A, EHMT1, DNMT3A, and BAT8) were statistically significant to breast cancer and...

An Improved Particle Swarm Optimization for Data Clustering

In recent years, clustering is still a popular analysis tool for data statistics. The data struct... more In recent years, clustering is still a popular analysis tool for data statistics. The data structure identifying from the large-scale data has become a very important issue in the data mining problem. In this paper, an improved particle swarm optimization based on Gauss chaotic map for clustering is proposed. Gauss chaotic map adopts a random sequence with a random starting point as a parameter, and relies on this parameter to update the positions and velocities of the particles. It provides the significant chaos distribution to balance the exploration and exploitation capability for search process. This easy and fast function generates a random seed processes, and further improve the performance of PSO due to their unpredictability. In the experiments, the eight different clustering algorithms were extensively compared on six test data. The results indicate that the performance of our proposed method is significantly better than the performance of other algorithms for data clusteri...

An Improved Particle Swarm Optimization to detect the SNP Barcode for Breast Cancer Prediction

The analyses of disease-associated polymorphisms by genome-wide association are important studies... more The analyses of disease-associated polymorphisms by genome-wide association are important studies for the detection of influences upon their interaction with other genetic factors. The gene can be examined in isolation without allowing potential interactions with other unknown factors, which might result in neglect of some disease influences. Single nucleotide polymorphism (SNP) barcodes can be applied for disease prediction and allow the detection of these influences. The particle swarm optimization (PSO) has been applied to detect the SNP barcode; however, PSO may fault to result in the non-significant SNP barcode. In this study, an improved PSO algorithm was introduced to detect the significant SNP barcodes for breast cancer prediction. A chaotic theory was applied to improve the PSO for finding the good SNP barcodes. The breast cancer datasets were simulated by using the genotype frequencies as described in the literature from steroid hormones and their signalling and metabolic ...

Effect of Baseline Characteristics and Tumor Burden on Vaspin Expression and Progressive Disease in Operable Colorectal Cancer

Diagnostics, 2020

Colorectal cancer is a highly heterogeneous malignancy in the Asian population, and it is conside... more Colorectal cancer is a highly heterogeneous malignancy in the Asian population, and it is considered an important prognostic factor for baseline characteristics, tumor burden, and tumor markers. This study investigated the effect of baseline characteristics and tumor burden on tumor marker expression and progressive disease in colorectal cancer by using partial least squares variance-based path modeling (PLS-PM). PLS-PM can be used to evaluate the complex relationship between prognostic variables and progressive disease status with a small sample of measurements and structural models. A total of 89 tissue samples of colorectal cancer were analyzed. Our results suggested that the expression of visceral adipose tissue-derived serpin (vaspin) is a potential indicator of colorectal cancer progression and may be affected by baseline characteristics such as age, sex, body mass index, and diabetes mellitus. Moreover, according to the characteristics of tumor burden, the expression of vaspi...

Epistasis Analysis Using an Improved Fuzzy C-Means-Based Entropy Approach

IEEE Transactions on Fuzzy Systems, 2019

Epistasis detection is vital to determining disease susceptibility in the human genome. With rapi... more Epistasis detection is vital to determining disease susceptibility in the human genome. With rapid advances in technology, multifactor dimensionality reduction (MDR) has become an effective algorithm for epistasis detection. Classification of high-risk (H) and low-risk (L) groups in MDR operations is a key topic but has not been thoroughly investigated. In this paper, we propose an improved fuzzy c-means-based entropy (FCME) approach to address the limitations of binary classification. For this approach, the degree of membership in MDR, referred to as FCMEMDR, was used. The FCME approach and MDR measure were integrated to enable more precise differentiation between similar frequencies of multifactor genotypes in cases of possible epistasis. We used the MDR measures of correct classification rate and likelihood ratio. Numerous simulated datasets were applied, and the experimental results revealed two measures of FCMEMDR with higher detection rates than those of other MDR-based algorithms. Our analysis of binary and fuzzy classifications in MDR operations may offer insights into the problem of uncertainty in H/L classification. Two measures of FCMEMDR detected significant instances of epistasis associated with coronary artery disease in the Wellcome Trust Case Control Consortium dataset. FCMEMDR is freely available at https://gitlab.com/yudalinemail/fcmemdr.

Detecting SNP–SNP Interactions in Imbalanced Case-Control Study

IEEE Access, 2019

SNP-SNP interactions are particularly informative biomarkers regarding the genetic components of ... more SNP-SNP interactions are particularly informative biomarkers regarding the genetic components of disease risk. However, SNP-SNP interaction identifications are yet limited in imbalanced case-control study. In this study, we proposed a multiobjective multifactor dimensionality reduction (MOMDR) based on three balancing approaches (BMOMDR), including (1) stratified K-fold crossvalidation; (2) balanced estimation of ratio between cases and controls; (3) balanced measures of SNP-SNP interactions, to effectively identify SNP-SNP interaction in imbalanced case-control study. BMOMDR was evaluated by extensive experiments on both simulated imbalanced case-control datasets and real genome-wide data from Wellcome Trust Case Control Consortium (WTCCC). For the simulated datasets, the results indicated that three balancing approaches can enhance the detection success rate of SNP-SNP interaction by MOMDR in imbalanced datasets. For WTCCC datasets, the results of SNP-SNP interaction detection obtained from BMOMDR revealed statistically significant (p <0.0001), revealing that BMOMDR can effectively identify SNP-SNP interaction in imbalanced case-control study. BMOMDR is freely available at http://shorturl.at/bluJS. INDEX TERMS SNP-SNP interactions, multiobjective approach multifactor dimensionality reduction, imbalanced case-control study.

Catfish Taguchi-Based Binary Differential Evolution Algorithm for Analyzing Single Nucleotide Polymorphism Interactions in Chronic Dialysis

IEEE Transactions on NanoBioscience, 2018

Single-nucleotide polymorphism (SNP)-SNP interactions are crucial for understanding the associati... more Single-nucleotide polymorphism (SNP)-SNP interactions are crucial for understanding the association between disease-related multifactorials for disease analysis. Existing statistical methods for determining such interactions are limited by the considerable computation required for evaluating all potential associations between disease-related multifactorials. Identifying SNP-SNP interactions is thus a major challenge in genetic association studies. This study proposes a catfish Taguchi-based binary differential evolution (CT-BDE) algorithm for identifying SNP-SNP interactions. In the search space, the catfish effect prevents the premature convergence of the population, and the Taguchi method improves the search ability of the BDE algorithm. Hence, the proposed algorithm enables obtaining a favorable solution regarding the identification of high-order SNP-SNP interactions. Additionally, the proposed algorithm applies an effective fitness function derived from a multifactor dimensionality reduction (MDR) operation to evaluate the solutions from BDE-based algorithms. Simulated and real data sets were used to evaluate the ability of several BDE-based algorithms in identifying specific SNP-SNP interactions. We compared the fitness function derived from the MDR operation with that derived according to the difference between cases and controls, by using the different BDE-based algorithms. The results showed that the proposed CT-BDE algorithm applying the fitness function derived from the MDR operation exhibited a superior ability in identifying SNP-SNP interactions compared with the other BDE-based algorithms.

Genetic Algorithm Combined with a Local Search Method for Identifying Susceptibility Genes

Journal of Artificial Intelligence and Soft Computing Research, 2016

Detecting genetic association models between single nucleotide polymorphisms (SNPs) in various di... more Detecting genetic association models between single nucleotide polymorphisms (SNPs) in various disease-related genes can help to understand susceptibility to disease. Statistical tools have been widely used to detect significant genetic association models, according to their related statistical values, including odds ratio (OR), chi-square test (χ2), p-value, etc. However, the high number of computations entailed in such operations may limit the capacity of such statistical tools to detect high-order genetic associations. In this study, we propose lsGA algorithm, a genetic algorithm based on local search method, to detect significant genetic association models amongst large numbers of SNP combinations. We used two disease models to simulate the large data sets considering the minor allele frequency (MAF), number of SNPs, and number of samples. The three-order epistasis models were evaluated by chi-square test (χ2) to evaluate the significance (P-value < 0.05). Analysis results sh...

A Hybrid Approach for CpG Island Detection in the Human Genome

PLOS ONE, 2016

Background CpG islands have been demonstrated to influence local chromatin structures and simplif... more Background CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging. Methodology/Principal Findings A novel procedure is proposed to detect CpG islands by combining clustering technology with the sliding-window method (PSO-based). Clustering technology is used to detect the locations of all possible CpG islands and process the data, thus effectively obviating the need for the extensive and unnecessary processing of DNA fragments, and thus improving the efficiency of sliding-window based particle swarm optimization (PSO) search. This proposed approach, named ClusterPSO, provides versatile and highly-sensitive detection of CpG islands in the human genome. In addition, the detection efficiency of ClusterPSO is compared with eight CpG island detection methods in the human genome. Comparison of the detection efficiency for the CpG islands in human genome, including sensitivity, specificity, accuracy, performance coefficient (PC), and correlation coefficient (CC), Clus-terPSO revealed superior detection ability among all of the test methods. Moreover, the combination of clustering technology and PSO method can successfully overcome their respective drawbacks while maintaining their advantages. Thus, clustering technology could be hybridized with the optimization algorithm method to optimize CpG island detection. Conclusion/Significance The prediction accuracy of ClusterPSO was quite high, indicating the combination of CpGcluster and PSO has several advantages over CpGcluster and PSO alone. In addition, ClusterPSO significantly reduced implementation time.

Determination of the SNP-SNP Interaction between Breast Cancer Related Genes to Analyze the Disease Susceptibility

International Journal of Machine Learning and Computing, 2014

Investigation of the single nucleotide polymorphism (SNP)-SNP interaction model can facilitate th... more Investigation of the single nucleotide polymorphism (SNP)-SNP interaction model can facilitate the analysis of the susceptibility to disease. The model explains the risk of association between the genotypes and the disease in case-control study. Thus, many mathematic methods are widely applied to identify the statistically significant model such as odds ratio (OR), chi-square test, and error rate. However, a huge number of data sets have been found to limit the statistical methods to identify the significant model. In this study, we propose a novel statistical method, complementary-logic particle swarm optimization (CLPSO), to increase the efficiency of significant model identification in case-control study. The complementary-logic is implemented to improve the PSO search ability and identify a better SNP-SNP interaction model. Six important breast cancer genes including 23 SNPs and simulated huge number of data sets were selected as the test data sets. The methods of PSO and CLPSO were applied on the identification of SNP-SNP interactions in the two-way to five-way. In results, the OR evaluates the breast cancer risk of the identified SNP-SNP interaction model. Compared to the corresponding non-interaction model, if the OR value is greater than 1 that indicates the model is significant risk between cases and controls. The results showed that CLPSO is able to identify the significant models for specific SNP-SNP interaction of two-way to five-way (OR value: 1.153-1.391; confidence interval (CI): 1.05-1.79; p-value: 0.01-0.003). The model suggests that the genes ESR1, PGR, and SHBG may be an important role in the interactive effects to breast cancer. In addition, we compared the search abilities of PSO and CLPSO for identification of the significant model. Results revealed that CLPSO can identify better model with difference values between cases and controls than PSO; it suggests CLPSO can be used to identify a better SNP-SNP interaction models. Index Terms-Single nucleotide polymorphism (SNP), particle swarm optimization (PSO), breast cancer. I. INTRODUCTION SNP is an common bio-marker in genomes, and it has widely used in the investigation of association analysis of diseases [1], cancers [2], and pharmacogenomics [3].

Limited-Parameter Optimization for PtRNASS using Chaotic Particle Swarm Optimization

Odds ratio based complementary-logic particle swarm optimization for multiple gene-gene interactions in breast cancer susceptibility

A Method of Predicting tRNA Secondary Structure from Nucleotide Sequences

PtRNASS: Prediction of tRNA Secondary Structure from Nucleotide Sequences

IAENG International Journal of Computer Science