Analysis of high-order SNP barcodes in mitochondrial D-loop for chronic dialysis susceptibility

Yang, Cheng‐Hong; Lin, Yu‐Da; Chuang, Li‐Yeh; Chang, Hsueh-Wei

doi:10.1016/j.jbi.2016.08.009

Cited by 12 publications

(3 citation statements)

References 28 publications

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“…DE is a population-based random function optimizer that was proposed by Storn and Price [28]. DE has been widely used in optimization problems [29][30][31]. According to the previous literature, DE has better performance and more stability than a genetic algorithm (GA) or a particle swarm optimization (PSO) algorithm [32] and is capable of achieving a solution superior to those of other algorithms.…”

Section: Application Of Fsdesvrmentioning

confidence: 99%

Forecasting outbound student mobility: A machine learning approach

Yang

Chen

et al. 2020

PLoS ONE

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Background A country's ability to become a prominent knowledge economy is tied closely to its ability to acquire skilled people who can compete internationally while resolving challenges of the future. To equip students with competence that can only by gained by being immersed in a foreign environment, outbound mobility is vital. Methods To analyze outbound student mobility in Taiwan using time series methods, this study aims to propose a hybrid approach FSDESVR which combines feature selection (FS) and support vector regression (SVR) with differential evolution (DE). FS and a DE algorithm were used for selecting reliable input features and determining the optimal initial parameters of SVR, respectively, to achieve high forecast accuracy. Results The proposed approach was examined using a dataset of outbound Taiwanese student mobility to ten countries between 1998 and 2018. Without the requirements of any special conditions for the proprieties of the objective function and constraints, the FSDESVR model retained the advantage of FS, SVR, and DE. A comparison of the FSDESVR model and other forecasting models revealed that FSDESVR provided the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) results for all the analyzed nations. The experimental results indicate that FSDESVR achieved higher forecasting accuracy than the compared models from the literature. Conclusion With the recognition of outbound values, key findings of Taiwanese outbound student mobility, and accurate application of the FSDESVR model, education administration units are exposed to a more in-depth view of future student mobility, which enables the implement of a more accurate education curriculum.

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Section: Application Of Fsdesvrmentioning

confidence: 99%

Forecasting outbound student mobility: A machine learning approach

Yang

Chen

et al. 2020

PLoS ONE

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“…2) Objective function 2: CCR fuzzy assesses the proportion of correctly classified individuals with an m-locus combination. CCR fuzzy is formulated as per (8).…”

Section: Momdr Processmentioning

confidence: 99%

Fuzzy Logic System Application for Detecting SNP-SNP Interaction

2020

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The identification of interactions between single-nucleotide polymorphisms (SNP-SNP interactions) is crucial for determining human genetic disease susceptibility. With rapid technological advancements, multiobjective multifactor dimensionality reduction (MOMDR) measurements have achieved high detection success rates. However, the classification of high-or low-risk groups is central to MOMDR and has yet to be extensively studied. To address limitations in binary classification, we propose an improved fuzzy sigmoid (FS) approach that uses membership degrees in MOMDR, thus denoting it as FSMOMDR. For determining the interval of membership, our improved FS approach assesses the distance between the i th multifactor class and outcome (cases and controls). Thus, the improved FS approach enables MOMDR algorithms to determine the membership degrees of high-and low-risk groups in each multifactor class because the two-element set is extended to a specified membership interval. Moreover, the improved FS approach can handle uncertain information, which thus enables the effective detection of the m-locus combinations with similar distributions. FSMOMDR measurements can also distinguish similar frequencies among genotype combinations, thus enabling the detection of more significant SNP-SNP interactions. On the basis of the classification accuracy rate of MOMDR and results obtained from the analysis of several test data sets, we determined FSMOMDR to be superior to other MDR-based methods with respect to detection success rate. The results indicate that binary and fuzzy classifications involving MOMDR can provide insight into uncertainty in risk classification. Thus, FSMOMDR could successfully detect SNP-SNP interactions in coronary artery disease in a large data set obtained from the Wellcome Trust Case Control Consortium. We could successfully reduce uncertain information in MDR and thus suggest that membership based on the improved sigmoid function can be used to identify SNP-SNP interactions as well as obtain content knowledge.

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“…In the human genome, SNPs are the most common sequence variations [17]. SNPs are widely used as suitable markers in association studies of several diseases [18], cancers [19], and in pharmacogenomics [20]. Recently, studies have suggested that SNPs of CYP26 family genes, such as CYP26A1 , CYP26B1 , and CYP26C1 , were associated with susceptibility to oral and pharyngeal cancers [21, 22].…”

Section: Introductionmentioning

confidence: 99%

Application of simulation-based CYP26 SNP-environment barcodes for evaluating the occurrence of oral malignant disorders by odds ratio-based binary particle swarm optimization: A case-control study in the Taiwanese population

Chen

Chuang

et al. 2019

PLoS ONE

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Introduction Genetic polymorphisms and social factors (alcohol consumption, betel quid (BQ) usage, and cigarette consumption), both separately or jointly, play a crucial role in the occurrence of oral malignant disorders such as oral and pharyngeal cancers and oral potentially malignant disorders (OPMD). Material and methods Simultaneous analyses of multiple single nucleotide polymorphisms (SNPs) and environmental effects on oral malignant disorders are essential to examine, albeit challenging. Thus, we conducted a case-control study (N = 576) to analyze the risk of occurrence of oral malignant disorders by using binary particle swarm optimization (BPSO) with an odds ratio (OR)-based method. Results We demonstrated that a combination of SNPs ( CYP26B1 rs887844 and CYP26C1 rs12256889) and socio-demographic factors (age, ethnicity, and BQ chewing), referred to as the combined effects of SNP-environment, correlated with maximal risk diversity of occurrence observed between the oral malignant disorder group and the control group. The risks were more prominent in the oral and pharyngeal cancers group (OR = 10.30; 95% confidence interval (CI) = 4.58–23.15) than in the OPMD group (OR = 5.42; 95% CI = 1.94–15.12). Conclusions Simulation-based “SNP-environment barcodes” may be used to predict the risk of occurrence of oral malignant disorders. Applying simulation-based “SNP-environment barcodes” may provide insight into the importance of screening tests in preventing oral and pharyngeal cancers and OPMD.

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Analysis of high-order SNP barcodes in mitochondrial D-loop for chronic dialysis susceptibility

Abstract: IDE generated significant SNP barcode with less computational complexity than the other algorithms, making IDE ideally suited for analysis of high-order SNP barcodes.

Cited by 12 publications

References 28 publications

Forecasting outbound student mobility: A machine learning approach

Forecasting outbound student mobility: A machine learning approach

Fuzzy Logic System Application for Detecting SNP-SNP Interaction

Application of simulation-based CYP26 SNP-environment barcodes for evaluating the occurrence of oral malignant disorders by odds ratio-based binary particle swarm optimization: A case-control study in the Taiwanese population

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