Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Fourier Harmonic Approach for Visualizing Temporal Patterns of Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Computational Statistics & Data Analysis
Artificial Intelligence in Medicine
Detection of Gene Expressions in Microarrays by Applying Iteratively Elastic Neural Net
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Multi-platform gene-expression mining and marker gene analysis
International Journal of Data Mining and Bioinformatics
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cDNA microarrays permit massively parallel gene expression analysis and have spawned a new paradigm in the study of molecular biology. One of the significant challenges in this genomic revolution is to develop sophisticated approaches to facilitate the visualization, analysis, and interpretation of the vast amounts of multi-dimensional gene expression data. We have applied self-organizing map (SOM) in order to meet these challenges. In essence, we utilize U-matrix and component planes in microarray data visualization and introduce general procedure for assessing significance for a cluster detected from U-matrix. Our case studies consist of two data sets. First, we have analyzed a data set containing 13,824 genes in 14 breast cancer cell lines. In the second case we show an example of the SOM in drug treatment of prostate cancer cells. Our results indicate that (1) SOM is capable of helping finding certain biologically meaningful clusters, (2) clustering algorithms could be used for finding a set of potential predictor genes for classification purposes, and (3) comparison and visualization of the effects of different drugs is straightforward with the SOM. In summary, the SOM provides an excellent format for visualization and analysis of gene microarray data, and is likely to facilitate extraction of biologically and medically useful information.