Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case–control association studies

Anekboon, Khantharat; Lursinsap, Chidchanok; Phimoltares, Suphakant; Fucharoen, Suthat; Tongsima, Sissades

doi:10.1016/j.compbiomed.2013.09.017

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…Genetic algorithm is a kind of metaheuristic algorithm for solving complex problems, and it has been used to solve successfully many combinatorial optimization problems in bioinformatics [10,11]. Some interrelated key techniques in devising the genetic algorithm are given as follows.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Haplotyping a single triploid individual based on genetic algorithm

Chen

2014

Bio-Medical Materials and Engineering

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The minimum error correction model is an important combinatorial model for haplotyping a single individual. In this article, triploid individual haplotype reconstruction problem is studied by using the model. A genetic algorithm based method GTIHR is presented for reconstructing the triploid individual haplotype. A novel coding method and an effectual hillclimbing operator are introduced for the GTIHR algorithm. This relatively short chromosome code can lead to a smaller solution space, which plays a positive role in speeding up the convergence process. The hill-climbing operator ensures algorithm GTIHR converge at a good solution quickly, and prevents premature convergence simultaneously. The experimental results prove that algorithm GTIHR can be implemented efficiently, and can get higher reconstruction rate than previous algorithms.

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Section: Genetic Algorithmmentioning

confidence: 99%

Haplotyping a single triploid individual based on genetic algorithm

Chen

2014

Bio-Medical Materials and Engineering

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“…When applying FS methods to GWAS, the SNPs are treated as the features, phenotypes are the labels, and the candidate SNPs are then selected according to their associations with phenotypes. Numerous FS methods have been applied in genetic association studies (Evans, 2010;Batnyam et al, 2013;Anekboon et al, 2014;Alzubi et al, 2017;An et al, 2017;Setiawan et al, 2018;Tsamardinos et al, 2019). For example, Evans (2010) combined two filter FS methods with classification methods in a machine-learning approach, and obtained strong association results.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the artificial features were classified by traditional classifiers. As an alternative combinational algorithm, Anekboon et al (2014) proposed a correlation-based FS method as a filter to first select a portion of the SNPs, followed by a wrapper phase to sequentially feed each of these SNPs into k-nearest neighbor, artificial neural network, and Ridge regression classifiers. Alzubi et al (2017) developed a hybrid FS method by combining conditional mutual information maximization and support vector machine-recursive feature elimination (SVM-RFE).…”

Section: Introductionmentioning

confidence: 99%

Genetic Variants Detection Based on Weighted Sparse Group Lasso

et al. 2020

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Identification of genetic variants associated with complex traits is a critical step for improving plant resistance and breeding. Although the majority of existing methods for variants detection have good predictive performance in the average case, they can not precisely identify the variants present in a small number of target genes. In this paper, we propose a weighted sparse group lasso (WSGL) method to select both common and lowfrequency variants in groups. Under the biologically realistic assumption that complex traits are influenced by a few single loci in a small number of genes, our method involves a sparse group lasso approach to simultaneously select associated groups along with the loci within each group. To increase the probability of selecting out low-frequency variants, biological prior information is introduced in the model by re-weighting lasso regularization based on weights calculated from input data. Experimental results from both simulation and real data of single nucleotide polymorphisms (SNPs) associated with Arabidopsis flowering traits demonstrate the superiority of WSGL over other competitive approaches for genetic variants detection.

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“…Many gastroenterology studies were performed using expert systems, such as externally validating gastronomical score and comparison with artificial neural networks (7), investigating Crohn's disease (CD) using a vacillating genetic algorithm and neural classifier (8), monitoring gastrointestinal motility using neural networks (9), and distinguishing among subtypes of neoplastic colorectal lesions using neural networks (10).…”

Section: Introductionmentioning

confidence: 99%

The rate of mucosal healing by azathioprine therapy and prediction by artificial systems

Hardalaç¹,

Başaranoğlu²,

Yüksel³

et al. 2015

Turk J Gastroenterol

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Background/Aims: We aimed to assess the effect of azathioprine on mucosal healing in patients with inflammatory bowel diseases (IBD). Artificial neural networks were applied to IBD data for predicting mucosal remission. Materials and Methods: Two thousand seven hundred patients with IBD were evaluated. According to the computer-based study, data of 129 patients with IBD were used. Artificial neural networks were performed and tested. Results: Endoscopic mucosal healing was found in 37% patients with IBD. Male gender group showed a negative impact on the efficacy of azathioprine (p<0.05). Responder patients with IBD were older than the nonresponder (p<0.05) patients. According to this study, the cascade-forward neural network study provides 79.1% correct results. In addition to a 0.16033 training error, mean square error (MSE) was taken at the 16 th epoch from the feed-forward back-propagation neural network. This neural structure, used for predicting mucosal remission with azathioprine, was also validated. Conclusion: Analyzing all parameters within each other to azathioprine therapy were shown that which parameters gave better healing were determined by statistical, and for the most weighted six input parameters, artificial neural network structures were constructed. In this study, feed-forward back-propagation and cascade-forward artificial neural network models were used.

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Extracting predictive SNPs in Crohn's disease using a vacillating genetic algorithm and a neural classifier in case–control association studies

Cited by 10 publications

References 18 publications

Haplotyping a single triploid individual based on genetic algorithm

Haplotyping a single triploid individual based on genetic algorithm

Genetic Variants Detection Based on Weighted Sparse Group Lasso

The rate of mucosal healing by azathioprine therapy and prediction by artificial systems

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