Independent subspace anlaysis (ISA) is a linear modelbased method which generalizes independent component analysis (ICA) by incorporating the invariant feature subspace into multidimensional ICA. In this paper we apply ISA to the problem of gene expression data analysis and show the useful behavior of the independent subspaces of gene expression data in the task of gene clustering and gene-gene interaction analysis. KEY WORDS DNA chip data, gene clustering, gene-gene interaction analysis, independent component analysis, independent subspace analysis.
Independent Subspace Analysis (ISA) denotes the task of linearly separating multivariate observation...
Abstract Background Independent Component Analysis (ICA) is a method that models gene expression dat...
Introduction: Independent Component Analysis (ICA) is a matrix factorization method for data dimensi...
This study presents an effective method of blindly classifying large amounts of gene expression data...
We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes f...
Independent Component Analysis (ICA) is an unsupervised machine learning algorithm which models a co...
DNA microarrays provide such a huge amount of data that unsupervised methods are required to reduce ...
High-throughput genome-widemeasurements of gene transcript levels have become available with the rec...
Abstract. Tree-dependent component analysis (TCA) is a generalization of independent component analy...
Abstract Background Gene microarray technology is an effective tool to investigate the simultaneous ...
peer reviewedWe propose a “time-biased” and a “space-biased” method for spatiotemporal independent ...
Motivation: The expression of genes is controlled by specific combinations of cellular variables. We...
<p>(A): The classical example of ICA is the “cocktail party problem,” where a number of microphones ...
We propose a new method for tumor classification from gene expression data, which mainly contains th...
DNA microarray gene expression and microarray based comparative genomic hybridization (aCGH) have be...
Independent Subspace Analysis (ISA) denotes the task of linearly separating multivariate observation...
Abstract Background Independent Component Analysis (ICA) is a method that models gene expression dat...
Introduction: Independent Component Analysis (ICA) is a matrix factorization method for data dimensi...
This study presents an effective method of blindly classifying large amounts of gene expression data...
We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes f...
Independent Component Analysis (ICA) is an unsupervised machine learning algorithm which models a co...
DNA microarrays provide such a huge amount of data that unsupervised methods are required to reduce ...
High-throughput genome-widemeasurements of gene transcript levels have become available with the rec...
Abstract. Tree-dependent component analysis (TCA) is a generalization of independent component analy...
Abstract Background Gene microarray technology is an effective tool to investigate the simultaneous ...
peer reviewedWe propose a “time-biased” and a “space-biased” method for spatiotemporal independent ...
Motivation: The expression of genes is controlled by specific combinations of cellular variables. We...
<p>(A): The classical example of ICA is the “cocktail party problem,” where a number of microphones ...
We propose a new method for tumor classification from gene expression data, which mainly contains th...
DNA microarray gene expression and microarray based comparative genomic hybridization (aCGH) have be...
Independent Subspace Analysis (ISA) denotes the task of linearly separating multivariate observation...
Abstract Background Independent Component Analysis (ICA) is a method that models gene expression dat...
Introduction: Independent Component Analysis (ICA) is a matrix factorization method for data dimensi...