Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
Adaptive double self-organizing maps for clustering gene expression profiles
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
An integrated tool for microarray data clustering and cluster validity assessment
Proceedings of the 2004 ACM symposium on Applied computing
EURASIP Journal on Applied Signal Processing
Cancer outcome prediction by cluster-based artificial immune networks
BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Expert Systems with Applications: An International Journal
A method of tumor classification based on wavelet packet transforms and neighborhood rough set
Computers in Biology and Medicine
47Glioblastoma gene expression profile diagnostics by the artificial neural networks
Optical Memory and Neural Networks
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The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Current high-throughput technology such as microarrays is generating an overwhelming amount of data of biological systems at the molecular and cellular level. To adequately organize, maintain, analyze and interpret this deluge of information the adaptation of existing and the development of new computational methodologies and tools is required. The principal approach to analyzing and interpreting biological data is to abstract them into logical structures that support and incrementally promote the development of a more general conceptual framework for characterizing, explaining, and predicting processes in living systems. Cluster analysis refers to a computing methodology that discovers and describes meaningful patterns or structures in data. Generally, cluster algorithms are governed by a learning-by-observation process. A plethora of specific algorithms has been suggested in the literature. In the context of microarray gene expression profiling of tumors, this work describes a comparative study of five clustering methods.