We present the use of KICA to perform clustering of gene expression data. Comparison experiments between KICA and two other methods, PCA and ICA, ...
Abstract. We present the use of KICA to perform clustering of gene expression data. Comparison experiments between KICA and two other methods, PCA and. ICA ...
Independent component analysis (ICA) methods have received growing attention as effective data-mining tools for microarray gene expression data.
In this review, we summarize and compare single-cell RNA sequencing technologies, that were developed since 2009, to facilitate a well-informed choice of ...
Kernel principal component analysis (KPCA) has been applied to data clustering and graphic cut in the last couple of years. This paper discusses the ...
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Recently, kernel approaches have proven to be good for classification such type of data. Kernel independent component analysis (KICA) is the nonlinear form of ...
We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model ...
This paper discusses the application of KPCA to microarray data clustering. A new algorithm based on KPCA and fuzzy C-means is proposed. Experiments with ...
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Kernel independent component analysis is applied to classify the features that selected by KICA and this method has been compared to kernel principle ...
We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of ...