@article {423,
	title = {Microarray Data classified by Artificial Neural Networks},
	journal = {Methods in molecular biology (Clifton, N.J.)},
	volume = {382},
	year = {2007},
	month = {2007},
	pages = {345-72},
	abstract = {Systems biology has enjoyed explosive growth in both the number of people participating in this area of research and the number of publications on the topic. The field of systems biology encompasses the in silico analysis of high-throughput data as provided by DNA or protein microarrays. Along with the increasing availability of microarray data, attention is focused on methods of analyzing the expression rates. One important type of analysis is the classification task, for example, distinguishing different types of cell functions or tumors. Recently, interest has been awakened toward artificial neural networks (ANN), which have many appealing characteristics such as an exceptional degree of accuracy. Nonlinear relationships or independence from certain assumptions regarding the data distribution are also considered. The current work reviews advantages as well as disadvantages of neural networks in the context of microarray analysis. Comparisons are drawn to alternative methods. Selected solutions are discussed, and finally algorithms for the effective combination of multiple ANNs are presented. The development of approaches to use ANN-processed microarray data applicable to run cell and tissue simulations may be slated for future investigation.},
	keywords = {Algorithms, Gene Expression Profiling, Neural Networks (Computer), Oligonucleotide Array Sequence Analysis, Pattern Recognition, Automated, Software, Systems Biology},
	issn = {1064-3745},
	doi = {10.1007/978-1-59745-304-2_22},
	author = {Linder, Roland and Richards, Tereza and Wagner, Mathias}
}
