Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Self-Organizing Maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Clinical Applications of Artificial Neural Networks
Clinical Applications of Artificial Neural Networks
IEEE Transactions on Information Technology in Biomedicine
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
Self-organizing neural networks to support the discovery of DNA-binding motifs
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
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Recent progress in biology and medical sciences has led to an explosive growth of biomedical data. Extracting relevant knowledge from such volumes of data represents an enormous challenge and opportunity. This paper assesses several approaches to improving neural network-based biomedical pattern discovery and visualization. It focuses on unsupervised classification problems, as well as on interactive and iterative methods to display, identify and validate potential relevant patterns. Clustering and pattern visualization models were based on the adaptation of a self-adaptive neural network known as Growing Self Organizing Maps. These models provided the basis for the implementation of hierarchical clustering, cluster validity assessment and a method for monitoring learning processes (cluster formation). This framework was tested on an electrocardiogram beat data set and data consisting of DNA splice-junction sequences. The results indicate that these techniques may facilitate knowledge discovery tasks by improving key factors such as predictive effectiveness, learning efficiency and understandability of outcomes.