Self-organizing maps
Modeling dependencies in protein-DNA binding sites
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Interactive GSOM-Based approaches for improving biomedical pattern discovery and visualization
CIS'04 Proceedings of the First international conference on Computational and Information Science
Efficient automatic exact motif discovery algorithms for biological sequences
Expert Systems with Applications: An International Journal
Tumble tree: reducing complexity of the growing cells approach
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
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Identification of the short DNA sequence motifs that serve as binding targets for transcription factors is an important challenge in bioinformatics. Unsupervised techniques from the statistical learning theory literature have often been applied to motif discovery, but effective solutions for large genomic datasets have yet to be found. We present here three self-organizing neural networks that have applicability to the motif-finding problem. The core system in this study is a previously described SOM-based motif-finder named SOMBRERO. The motif-finder is integrated in this work with a SOM-based method that automatically constructs generalized models for structurally related motifs and initializes SOMBRERO with relevant biological knowledge. A self-organizing tree method that displays the relationships between various motifs is also presented, and it is shown that such a method can act as an effective structural classifier of novel motifs. The performance of the three self-organizing neural networks is evaluated here using various datasets.