Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine

Li, JiaRui; Lin, Liwu; Zhang, Yuhang; Xu, Yaochen; Liu, Min; Kong, Feng; Chen, Lei; Kong, Xiangyin; Huang, Tao; Cai, Yu-Dong

doi:10.1038/s41417-019-0105-y

Cited by 45 publications

(37 citation statements)

References 97 publications

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“…Classification Algorithm. Two robust machine learning techniques, i.e., SVM and RF, are applied to perform the prediction of DBPs, which have been widely used for many classification tasks in the field of computational biology [43][44][45][46]. SVM is an outstanding classification method that is used to deal with a binary pattern recognition problem [47].…”

Section: Featurementioning

confidence: 99%

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

Sang

Xiao

Zheng

et al. 2020

Computational and Mathematical Methods in Medicine

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Prediction of DNA-binding proteins (DBPs) has become a popular research topic in protein science due to its crucial role in all aspects of biological activities. Even though considerable efforts have been devoted to developing powerful computational methods to solve this problem, it is still a challenging task in the field of bioinformatics. A hidden Markov model (HMM) profile has been proved to provide important clues for improving the prediction performance of DBPs. In this paper, we propose a method, called HMMPred, which extracts the features of amino acid composition and auto-and cross-covariance transformation from the HMM profiles, to help train a machine learning model for identification of DBPs. Then, a feature selection technique is performed based on the extreme gradient boosting (XGBoost) algorithm. Finally, the selected optimal features are fed into a support vector machine (SVM) classifier to predict DBPs. The experimental results tested on two benchmark datasets show that the proposed method is superior to most of the existing methods and could serve as an alternative tool to identify DBPs.

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Section: Featurementioning

confidence: 99%

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

Sang

Xiao

Zheng

et al. 2020

Computational and Mathematical Methods in Medicine

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“…After the irrelevant features were removed, the relevant methylation and expression features were ranked based on their importance evaluated with MCFS (Monte Carlo Feature Selection) (Draminski et al, 2008). The MCFS was a widely used method to rank features based on classification trees (Chen et al, , 2019Pan et al, 2018Pan et al, , 2019aLi et al, 2019). First, for the d features, we selected s subsets and each subset included m features (m was much smaller than d).…”

Section: Evaluate the Importance Of Relevant Methylation And Expressimentioning

confidence: 99%

Integrative Analysis of Methylation and Gene Expression in Lung Adenocarcinoma and Squamous Cell Lung Carcinoma

Zhang

Zhou

Cheng³

et al. 2020

Front. Bioeng. Biotechnol.

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Lung cancer is a highly prevalent type of cancer with a poor 5-year survival rate of about 4-17%. Eighty percent lung cancer belongs to non-small-cell lung cancer (NSCLC). For a long time, the treatment of NSCLC has been mostly guided by tumor stage, and there has been no significant difference between the therapy strategy of lung adenocarcinoma (LUAD) and squamous cell lung carcinoma (SCLC), the two major subtypes of NSCLC. In recent years, important molecular differences between LUAD and SCLC are increasingly identified, indicating that targeted therapy will be more and more histologically specific in the future. To investigate the LUAD and SCLC difference on multi-omics scale, we analyzed the methylation and gene expression data together. With the Boruta method to remove irrelevant features and the MCFS (Monte Carlo Feature Selection) method to identify the significantly important features, we identified 113 key methylation features and 23 key gene expression features. HNF1B and TP63 were found to be dysfunctional on both methylation and gene expression levels. The experimentally determined interaction network suggested that TP63 may play an important role in connecting methylation genes and expression genes. Many of the discovered signature genes have been supported by literature. Our results may provide directions of precision diagnosis and therapy of LUAD and SCLC.

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“…The second stage was to determine the number of selected genes using the IFS method (Chen et al, 2018b;Chen et al, 2019b;Chen et al, 2019c;Chen et al, 2019d;Chen et al, 2019f;Li et al, 2019a;Pan et al, 2019a;Pan et al, 2019b;). To do so, 200 classifiers were constructed using top 1, top 2, top 200 genes.…”

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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Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine

Cited by 45 publications

References 97 publications

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

Integrative Analysis of Methylation and Gene Expression in Lung Adenocarcinoma and Squamous Cell Lung Carcinoma

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

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