Journal ArticleDOI
Minimum redundancy feature selection from microarray gene expression data.
Chris Ding,Hanchuan Peng +1 more
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How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes.Abstract:
How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their ...read more
Citations
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Journal ArticleDOI
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.
Journal ArticleDOI
A review of feature selection techniques in bioinformatics
TL;DR: A basic taxonomy of feature selection techniques is provided, providing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.
Journal ArticleDOI
A survey on feature selection methods
Girish Chandrashekar,Ferat Sahin +1 more
TL;DR: The objective is to provide a generic introduction to variable elimination which can be applied to a wide array of machine learning problems and focus on Filter, Wrapper and Embedded methods.
Proceedings Article
Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization
TL;DR: A new robust feature selection method with emphasizing joint l2,1-norm minimization on both loss function and regularization is proposed, which has been applied into both genomic and proteomic biomarkers discovery.
Journal ArticleDOI
Alterations of the human gut microbiome in liver cirrhosis
Nan Qin,Fengling Yang,Ang Li,Edi Prifti,Yanfei Chen,Li Shao,Jing Guo,Jian Yao,Lingjiao Wu,Jiawei Zhou,Ni Shujun,Lin Liu,Nicolas Pons,Jean-Michel Batto,Sean Kennedy,Pierre Leonard,Chunhui Yuan,Wenchao Ding,Yuanting Chen,Xinjun Hu,Beiwen Zheng,Guirong Qian,Wei Xu,S. Dusko Ehrlich,Shu-Sen Zheng,Lanjuan Li +25 more
TL;DR: The gut microbiome in liver cirrhosis is characterized by comparing 98 patients and 83 healthy control individuals and on the basis of only 15 biomarkers, a highly accurate patient discrimination index is created and validated on an independent cohort, suggesting microbiota-targeted biomarkers may be a powerful tool for diagnosis of different diseases.
References
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Multi-class protein fold recognition using support vector machines and neural networks.
Chris Ding,Inna Dubchak +1 more
TL;DR: This work investigated two new methods for protein fold prediction using the Support Vector Machine and the Neural Network learning methods as base classifiers, and examined many issues involved with large number of classes, including dependencies of prediction accuracy on the number of folds and on thenumber of representatives in a fold.
Journal ArticleDOI
Tumor classification by partial least squares using microarray gene expression data
Danh V. Nguyen,David M. Rocke +1 more
TL;DR: A novel analysis procedure for classifying (predicting) human tumor samples based on microarray gene expressions is proposed and PLS proves superior to the well known dimension reduction method of Principal Components Analysis (PCA).
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Tissue classification with gene expression profiles.
TL;DR: This work examines three sets of gene expression data measured across sets of tumor(s) and normal clinical samples, and presents results of performing leave-one-out cross validation (LOOCV) experiments on the three data sets, employing nearest neighbor classifier, SVM, AdaBoost and a novel clustering-based classification technique.
Journal ArticleDOI
Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer
John B. Welsh,Patrick P. Zarrinkar,Lisa M. Sapinoso,Suzanne G. Kern,Cynthia Behling,Bradley J. Monk,David J. Lockhart,Robert A. Burger,Garret M. Hampton +8 more
TL;DR: This study uses oligonucleotide microarrays with probe sets complementary to >6,000 human genes to identify genes whose expression correlated with epithelial ovarian cancer and demonstrates the rapidity with which large amounts of expression data can be generated.
Journal ArticleDOI
Classification of multiple cancer types by multicategory support vector machines using gene expression data.
Yoonkyung Lee,Cheol Koo Lee +1 more
TL;DR: The Multicategory SVM is introduced, which is a recently proposed extension of the binary SVM, and applied to multiclass cancer diagnosis problems, which renders the MSVM a viable alternative to other classification methods.