Journal Article10.1016/J.COMPBIOMED.2010.08.003
Gene expression data classification using locally linear discriminant embedding
TL;DR: This work is a meaningful attempt to analyze microarray data using manifold learning method; there should be much room for the application of manifold learning to bioinformatics due to its performance.
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About: This article is published in Computers in Biology and Medicine. The article was published on 01 Oct 2010. The article focuses on the topics: Data classification & Linear discriminant analysis.
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Citations
Dimension reduction methods for microarray data: a review
Rabia Aziz,C. K. Verma,Namita Srivastava +2 more
- 01 Mar 2017
TL;DR: The taxonomy of dimension reduction methods with their characteristics, evaluation criteria, advantages and disadvantages is described and a review of numerous dimension reduction approaches for microarray data is presented, mainly those methods that have been proposed over the past few years.
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Fault diagnosis method based on incremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier
TL;DR: The results indicate that the proposed fault diagnosis method outperforms the traditional methods and achieves higher diagnostic accuracy.
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Review article: Computational intelligence techniques in bioinformatics
TL;DR: It is shown how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification.
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A novel feature extraction approach based on ensemble feature selection and modified discriminant independent component analysis for microarray data classification
TL;DR: An ensemble schema for cancer diagnosis and classification that has three stages, a hybrid filter-based feature selection method using modified Bayesian logistic regression, Ttest and Fisher ratio is applied for selecting genes and mapped features are classified using SVM classifier.
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