@inproceedings{eebf29d0295947c2b5df89886688d33d,
title = "Sparse nonnegative matrix factorization with genetic algorithms for microarray analysis",
abstract = "Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. Gene expression profiles naturally conform to assumptions about data formats raised by NMF. However, it is known not to lead to unique results concerning the component signals extracted. In this paper we consider an extension of the NMF algorithm which provides unique solutions whenever the underlying component signals are sufficiently sparse. A new sparseness measure is proposed most appropriate to suitably transformed gene expression profiles. The resulting fitness function is discontinuous and exhibits many local minima, hence we use a genetic algorithm for its optimization. The algorithm is applied to toy data to investigate its properties as well as to a microarray data set related to Pseudo-Xanthoma Elasticum (PXE).",
author = "K. Stadlthanner and D. Lutter and Theis, {F. J.} and Lang, {E. W.} and Tom{\'e}, {A. M.} and P. Georgieva and Puntonet, {C. G.}",
year = "2007",
doi = "10.1109/IJCNN.2007.4370971",
language = "English",
isbn = "142441380X",
series = "IEEE International Conference on Neural Networks - Conference Proceedings",
pages = "294--299",
booktitle = "The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings",
note = "2007 International Joint Conference on Neural Networks, IJCNN 2007 ; Conference date: 12-08-2007 Through 17-08-2007",
}