Gene selection from microarray data for cancer classification—a machine learning approach

Wang, Yu; Tetko, Igor V.; Hall, Mark; Frank, Eibe; Facius, Axel; Mayer, Klaus; Mewes, Hans‐Werner

doi:10.1016/j.compbiolchem.2004.11.001

Cited by 348 publications

(216 citation statements)

References 25 publications

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“…The key idea of reliefF is to rate genes according to how well their values distinguish among instances of different classes that are near each other [for details, see ref. (19)]. The mean values, geometric mean and SEM were calculated using Sigma Plot 2000 (Jandel, Erkrath, Germany).…”

Section: Statisticsmentioning

confidence: 99%

A Radiation-Induced Gene Signature Distinguishes Post-Chernobyl from Sporadic Papillary Thyroid Cancers

Port

Boltze

Wang³

et al. 2007

Radiation Research

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Section: Statisticsmentioning

confidence: 99%

A Radiation-Induced Gene Signature Distinguishes Post-Chernobyl from Sporadic Papillary Thyroid Cancers

Port

Boltze

Wang³

et al. 2007

Radiation Research

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“…It can be observed that the proposed method is giving high classification accuracy on very small number of selected features. # Selected genes on TEST data) (%) Prediction strength+ SVMs (Furey et al, 2000) 25-1000 88-94 Discretization + decision rules (Tan and Gilbert, 2003) 1038 91 RCBT (Cong et al, 2005) 10-40 91 Neighbourhood analysis + weighted 50 85 voting (Golub et al, 1999) CBF + decision trees (Wang et al, 2005) Lung cancer Discretization + decision trees (Tan and Gilbert, 2003) 5365 93 Boosting (Li and Wong, 2003) unknown 81 Bagging (Li and Wong, 2003) unknown 88 RCBT (Cong et al, 2005) 10-40 98 C4.5 (Li and Wong, 2003 …”

Section: Resultsmentioning

confidence: 99%

“…The feature selection algorithms are considered to be an important way of identifying crucial genes. Various feature selection algorithms have been proposed in the literature with some advantages and disadvantages (Sharma et al, 2011b;Tan and Gilbert, 2003;Cong et al, 2005;Golub et al, 1999;Wang et al, 2005;Li and Wong, 2003;Thi et al, 2008;Yan and Zheng, 2007;Sharma et al, 2011a). These methods select important genes using some objective functions.…”

Section: Introductionmentioning

confidence: 99%

A Gene Selection Algorithm using Bayesian Classification Approach

Mohamed¹

2012

American Journal of Applied Sciences

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Abstract:In this study, we propose a new feature (or gene) selection algorithm using Bayes classification approach. The algorithm can find gene subset crucial for cancer classification problem. Problem statement: Gene identification plays important role in human cancer classification problem. Several feature selection algorithms have been proposed for analyzing and understanding influential genes using gene expression profiles. Approach: The feature selection algorithms aim to explore genes that are crucial for accurate cancer classification and also endure biological significance. However, the performance of the algorithms is still limited. In this study, we propose a feature selection algorithm using Bayesian classification approach. Results: This approach gives promising results on gene expression datasets and compares favorably with respect to several other existing techniques. Conclusion: The proposed gene selection algorithm using Bayes classification approach is shown to find important genes that can provide high classification accuracy on DNA microarray gene expression datasets.

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“…Previous research has demonstrated that this technology can be useful in the classification of cancers. Cancer microarray data normally contains a small number of samples which have a large number of gene expression levels as features are given by Wang et al (2005). Duan et al (2005) proposee a new feature selection method that uses a backward elimination procedure similar to that implemented in support vector machine recursive feature elimination (SVM-RFE).…”

Section: Literature Surveymentioning

confidence: 99%

Cancer Classification using Hybrid Fast Particle Swarm Optimization with Backpropagation Neural Network

Vimaladevi¹,

Kalaavathi²

2014

International Journal of Advanced Research in Computer and Comm

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: Cancer any type of malignant growth or tumor, caused by abnormal and uncontrolled cell division: it may spread through the lymphatic system or blood stream to other parts of the body .Cancer classification is known to have the keys for addressing the problems based on cancer diagnosis and drug discovery. The proposed method of DNA microarray technique has made continuous processing thousands of gene expressions possible. Using gene expression data, the researchers have started the performance to explore the possibilities of cancer classification. The various methods have been introduced in recent years with promising results. But there are still a lot of problem which need to be addressed and understood. The performance of combining the genetic algorithm and Hybrid Fast PSO-BPN method is used to solve the optimization problems. Hybrid Fast PSO-BPN method is used to improve the accuracy and better convergence rate of genetic algorithm and this method is better for local search. This proposed method is used to overcome from the problem of computational difficulties occur by ill-condition of the square penalty function. The experimental result shows that this proposed method is better in accurate result with less execution time.

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Gene selection from microarray data for cancer classification—a machine learning approach

Cited by 348 publications

References 25 publications

A Radiation-Induced Gene Signature Distinguishes Post-Chernobyl from Sporadic Papillary Thyroid Cancers

A Radiation-Induced Gene Signature Distinguishes Post-Chernobyl from Sporadic Papillary Thyroid Cancers

A Gene Selection Algorithm using Bayesian Classification Approach

Cancer Classification using Hybrid Fast Particle Swarm Optimization with Backpropagation Neural Network

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