MISEP Method for Postnonlinear Blind Source Separation

Chong

Wang

2011

Neural Comput & Applic

Self Cite

Microarrays are capable of detecting the expression levels of thousands of genes simultaneously. So, gene expression data from DNA microarray are characterized by many measured variables (genes) on only a few samples. One important application of gene expression data is to classify the samples. In statistical terms, the very large number of predictors or variables compared to small number of samples makes most of classical ''class prediction'' methods unemployable. Generally, this problem can be avoided by selecting only the relevant features or extracting new features containing the maximal information about the class label from the original data. In this paper, a new method for gene selection based on independent variable group analysis is proposed. In this method, we first used t-statistics method to select a part of genes from the original data. Then, we selected the key genes from the selected genes for tumor classification using IVGA. Finally, we used SVM to classify tumors based on the key genes selected using IVGA. To validate the efficiency, the proposed method is applied to classify three different DNA microarray data sets. The prediction results show that our method is efficient and feasible.

Section: Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gene selection using independent variable group analysis for tumor classification

Chong

Wang

2011

Neural Comput & Applic

Self Cite

“…ICA can reduce the effects of noise or artifacts on the signal and is efficient for separating mixed signals [18], [21]. Recently, more and more successful applications of ICA into microarray data analysis were reported to extract expression modes of genes [19], [20], [22], [23].…”

Section: Gene Selection By Icamentioning

confidence: 99%

Tumor Clustering Using Nonnegative Matrix Factorization With Gene Selection

IEEE Trans. Inform. Technol. Biomed.

Huang

Zhang

et al. 2009

194

Abstract-Tumor clustering is becoming a powerful method in cancer class discovery. Nonnegative matrix factorization (NMF) has shown advantages over other conventional clustering techniques. Nonetheless, there is still considerable room for improving the performance of NMF. To this end, in this paper, gene selection and explicitly enforcing sparseness are introduced into the factorization process. Particularly, independent component analysis is employed to select a subset of genes so that the effect of irrelevant or noisy genes can be reduced. The NMF and its extensions, sparse NMF and NMF with sparseness constraint, are then used for tumor clustering on the selected genes. A series of elaborate experiments are performed by varying the number of clusters and the number of selected genes to evaluate the cooperation between different gene selection settings and NMF-based clustering. Finally, the experiments on three representative gene expression datasets demonstrated that the proposed scheme can achieve better clustering results.Index Terms-Clustering, gene expression data, independent component analysis (ICA), nonnegative matrix factorization (NMF), tumor.

“…For the convenience of expression, we use the word metasample throughout the paper. The metasamples can be computed by using SVD, PCA, NMF or other linear or nonlinear models such as ICA and nonlinear ICA [23,24].…”

Section: Introductionmentioning

confidence: 99%

Metasample-Based Sparse Representation for Tumor Classification

IEEE/ACM Trans. Comput. Biol. and Bioinf.

Zhang

et al. 2011

110

A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by 1 l -norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR based method for tumor classification using gene expression data. A set of metasamples are extracted from the training samples, and then an input testing sample is represented as the linear combination of these metasamples by l 1 -regularized least square method. Classification is achieved by using a discriminating function defined on the representation coefficients. Since l 1 -norm minimization leads to a sparse solution, the proposed method is called metasample based SR classification (MSRC).Extensive experiments on publicly available gene expression datasets show that MSRC is efficient for tumor classification, achieving higher accuracy than many existing representative schemes.