A Hybrid Feature Selection Method for Complex Diseases SNPs

Alzubi, Raid; Ramzan, Naeem; Alzoubi, Hadeel; Amira, Abbes

doi:10.1109/access.2017.2778268

Cited by 49 publications

(27 citation statements)

References 33 publications

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“…A small subset of features has a strong correlation among each other and with the class label. Therefore, various techniques for feature selection have been successfully applied to reduce the feature space [51,[84][85][86][87][88]. It is worth mentioning that chi-square [89] is the most popular feature selection method in the reviewed literature.…”

Section: Feature Selectionmentioning

confidence: 99%

A Review of Automatic Phenotyping Approaches using Electronic Health Records

et al. 2019

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Electronic Health Records (EHR) are a rich repository of valuable clinical information that exist in primary and secondary care databases. In order to utilize EHRs for medical observational research a range of algorithms for automatically identifying individuals with a specific phenotype have been developed. This review summarizes and offers a critical evaluation of the literature relating to studies conducted into the development of EHR phenotyping systems. This review describes phenotyping systems and techniques based on structured and unstructured EHR data. Articles published on PubMed and Google scholar between 2013 and 2017 have been reviewed, using search terms derived from Medical Subject Headings (MeSH). The popularity of using Natural Language Processing (NLP) techniques in extracting features from narrative text has increased. This increased attention is due to the availability of open source NLP algorithms, combined with accuracy improvement. In this review, Concept extraction is the most popular NLP technique since it has been used by more than 50% of the reviewed papers to extract features from EHR. High-throughput phenotyping systems using unsupervised machine learning techniques have gained more popularity due to their ability to efficiently and automatically extract a phenotype with minimal human effort.

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Section: Feature Selectionmentioning

confidence: 99%

A Review of Automatic Phenotyping Approaches using Electronic Health Records

et al. 2019

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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%

“…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). An et al (2017) used a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps to improve the classifier learning.…”

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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“…Using machine learning methods for data mining of SNP pathogenic sites has become a research hotspot in the field of bioinformatics [ 16 , 31 ]. Due to the flexibility of modeling different data sources [ 32 ], the SVM algorithm is used for many complex diseases [ 33 – 35 ].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of alcohol dependence based on the convolutional neural network

et al. 2020

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In this paper, a total of 20 sites of single nucleotide polymorphisms (SNPs) on the serotonin 3 receptor A gene (HTR3A) and B gene (HTR3B) are used for feature fusion with age, education and marital status information, and the grid search-support vector machine (GS-SVM), the convolutional neural network (CNN) and the convolutional neural network combined with long and short-term memory (CNN-LSTM) are used to classify and discriminate between alcohol-dependent patients (AD) and the non-alcohol-dependent control group. The results show that 19 SNPs combined with academic qualifications have the best discrimination effect. In the GS-SVM, the area under the receiver operating characteristic (ROC) curve (AUC) is 0.87, the AUC of CNN-LSTM is 0.88, and the performance of the CNN model is the best, with an AUC of 0.92. This study shows that the CNN model can more accurately discriminate AD than the SVM to treat patients in time.

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A Hybrid Feature Selection Method for Complex Diseases SNPs

Cited by 49 publications

References 33 publications

A Review of Automatic Phenotyping Approaches using Electronic Health Records

A Review of Automatic Phenotyping Approaches using Electronic Health Records

Genetic Variants Detection Based on Weighted Sparse Group Lasso

Discrimination of alcohol dependence based on the convolutional neural network

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