Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
A fast fixed-point algorithm for independent component analysis
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind matrix decomposition via genetic optimization of sparseness and nonnegativity constraints
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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High-throughput genome-wide measurements of gene transcript levels have become available with the recent development of microarray technology. Intelligent and efficient mathematical and computational analysis tools are needed to read and interpret the information content buried in those large scale gene expression patterns at various levels of resolution. But the development of such methods is still in its infancy. Modern machine learning and data mining techniques based on information theory, like independent component analysis (ICA), consider gene expression patterns as a superposition of independent expression modes which are considered putative independent biological processes. We focus on two widely used ICA algorithms to blindly decompose gene expression profiles into independent component profiles representing underlying biological processes. These exploratory methods will be capable of detecting similarity, locally or globally, in gene expression patterns and help to group genes into functional categories - for example, genes that are expressed to a greater or lesser extent in response to a drug or an existing disease.