The early detection of asthma based on blood gene expression

Wang, Shaobin; Huang, Tao

doi:10.1007/s11033-018-4463-6

Cited by 19 publications

(18 citation statements)

References 54 publications

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“…Based on the ranked mRMR site list, each time, the top r sites

were chosen to construct a SVM (Support Vector Machine) classifier and its accuracy evaluated with LOOCV ( Chen et al, 2014 ; Zhang N. et al, 2015 ; Cheng et al, 2016 ; Li et al, 2018 ; Wang and Huang, 2019 ) was recorded. SVM is classical machine learning classifier with a wide range of applications in biomedicine ( Chen et al, 2017c , 2019b ; Li et al, 2018 , 2019b ; Sun et al, 2018 ; Pan et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

“…It can identify a small but well performed methylation signature. What’s more, an SVM (Support Vector Machine) ( Chen et al, 2017c , 2019a , b , d ; Sun et al, 2018 ; Li et al, 2019b ; Pan et al, 2019 ) OA classifier was built based on these 12 methylation sites and it can perfectly classify the OA hip samples, control hip samples and OA knee samples evaluated with LOOCV (Leave-One Out-Cross Validation) ( Chen et al, 2014 ; Zhang N. et al, 2015 ; Cheng et al, 2016 ; Li et al, 2018 ; Wang and Huang, 2019 ). Although the model needs to be validated on independent large dataset, these 12 methylation sites provided clues for the mechanisms of OA.…”

Section: Introductionmentioning

confidence: 99%

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The Methylation Pattern for Knee and Hip Osteoarthritis

Shou

Wang

et al. 2020

Front. Cell Dev. Biol.

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Osteoarthritis is one of the most prevalent chronic joint diseases for middle-aged and elderly people. But in recent years, the number of young people suffering from the disease increases quickly. It is known that osteoarthritis is a common degenerative disease caused by the combination and interaction of many factors such as natural and environmental factors. DNA methylations reflect the effects of environmental factors. Several researches on DNA methylation at specific genes in OA cartilage indicated the great potential roles of DNA methylation in OA. To systematically investigate the methylation pattern in knee and hip osteoarthritis, we analyzed the methylation profiles in cartilage of 16 OA hip samples, 19 control hip samples and 62 OA knee samples. 12 discriminative methylation sites were identified using advanced minimal Redundancy Maximal Relevance (mRMR) and Incremental Feature Selection (IFS) methods. The SVM classifier of these 12 methylation sites from genes like MEIS1, GABRG3, RXRA, and EN1, can perfectly classify the OA hip samples, control hip samples and OA knee samples evaluated with LOOCV (Leave-One Out-Cross Validation). These 12 methylation sites can not only serve as biomarker, but also provide underlying mechanism of OA.

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“…Based on the ranked mRMR site list, each time, the top r sites

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Methylation Pattern for Knee and Hip Osteoarthritis

Shou

Wang

et al. 2020

Front. Cell Dev. Biol.

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“…Therefore, we adopted machine learning based multiple feature selection strategies to objectively select the optimal heart failure signature. The machine learning-based methods have been widely used and achieved great success in biomarker discovery (Wang and Huang, 2019;Li et al, 2020a,b;Yuan et al, 2020;Zhang et al, 2020a,b;Zhu et al, 2020).…”

Section: Select the Genes Based On Their Importance To Classify The Hmentioning

confidence: 99%

Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure

Lin²,

2020

Front. Physiol.

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Heart failure (HF) is a serious condition in which the support of blood pumped by the heart is insufficient to meet the demands of body at a normal cardiac filling pressure. Approximately 26 million patients worldwide are suffering from heart failure and about 17–45% of patients with heart failure die within 1-year, and the majority die within 5-years admitted to a hospital. The molecular mechanisms underlying the progression of heart failure have been poorly studied. We compared the gene expression profiles between patients with heart failure ( n = 177) and without heart failure ( n = 136) using multiple feature selection strategies and identified 38 HF signature genes. The support vector machine (SVM) classifier based on these 38 genes evaluated with leave-one-out cross validation (LOOCV) achieved great performance with sensitivity of 0.983 and specificity of 0.963. The network analysis suggested that the hub gene SMOC2 may play important roles in HF. Other genes, such as FCN3 , HMGN2 , and SERPINA3 , also showed great promises. Our results can facilitate the early detection of heart failure and can reveal its molecular mechanisms.

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“…First, the genes were ranked based on not only their relevance with mutation samples, but also their redundancy among genes using the mRMR algorithm (Peng et al, 2005). It had a wide range of applications in bioinformatics for feature selection (Chen et al, 2018c;Chen et al, 2019e;Li et al, 2019b;Wang and Huang, 2019a). As the equation shown below, Ω s , Ω t and Ω were the set of m selected genes, n tobe-selected genes, and all m+n genes, respectively.…”

Section: Two Stage Feature Selection Approachmentioning

confidence: 99%

“…We tried several different classifiers: (1) SVM (Support Vector Machine) (Jiang et al, 2019;Yan et al, 2019;Chen et al, 2019a;Li et al, 2019a;Pan et al, 2019a;Wang and Huang, 2019b;Chen et al, 2019d), (2) 1NN (1 Nearest Neighbor) (Lei et al, 2013;Chen et al, 2016;Wang et al, 2017a), (3) 3NN (3 Nearest Neighbors), (4) 5NN (5 Nearest Neighbors), (5) Decision Tree (DT) (Huang et al, 2008;Huang et al, 2011;Chen et al, 2015), (6) Neural Network (NN) (Liu et al, 2017;Pan et al, 2018;Chen et al, 2019e). The function svm from R package e1071, function knn from R package class, function rpart from R package rpart, function nnet from R package nnet were used to apply these classification algorithms.…”

Section: Two Stage Feature Selection Approachmentioning

confidence: 99%

The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

Zhang

et al. 2020

Front. Genet.

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Lung cancer is a common malignant cancer. Kirsten rat sarcoma oncogene (KRAS) mutations have been considered as a key driver for lung cancers. KRAS p.G12C mutations were most predominant in NSCLC which was comprised about 11-16% of lung adenocarcinomas (p.G12C accounts for 45-50% of mutant KRAS). But it is still not clear how the KRAS mutation triggers lung cancers. To study the molecular mechanisms of KRAS mutation in lung cancer. We analyzed the gene expression profiles of 156 KRAS mutation samples and other negative samples with two stage feature selection approach: (1) minimal Redundancy Maximal Relevance (mRMR) and (2) Incremental Feature Selection (IFS). At last, 41 predictive genes for KRAS mutation were identified and a KRAS mutation predictor was constructed. Its leave one out cross validation MCC was 0.879. Our results were helpful for understanding the roles of KRAS mutation in lung cancer.

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The early detection of asthma based on blood gene expression

Cited by 19 publications

References 54 publications

The Methylation Pattern for Knee and Hip Osteoarthritis

The Methylation Pattern for Knee and Hip Osteoarthritis

Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure

The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

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