Fuzzy C-Means Based DNA Motif Discovery
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Synchronization between Two Different Chaotic Neural Networks with Fully Unknown Parameters
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
GAPK: genetic algorithms with prior knowledge for motif discovery in DNA sequences
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Overlap-Based Similarity Metrics for Motif Search in DNA Sequences
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
MotifMiner: a table driven greedy algorithm for DNA motif mining
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Robust state estimation for neural networks with discontinuous activations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SOMIX: motifs discovery in gene regulatory sequences using self-organizing maps
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
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In this paper, we study the problem of motif discoveries in unaligned DNA and protein sequences. The problem of motif identification in DNA and protein sequences has been studied for many years in the literature. Major hurdles at this point include computational complexity and reliability of the search algorithms. We propose a self-organizing neural network structure for solving the problem of motif identification in DNA and protein sequences. Our network contains several layers, with each layer performing classifications at different levels. The top layer divides the input space into a small number of regions and the bottom layer classifies all input patterns into motifs and nonmotif patterns. Depending on the number of input patterns to be classified, several layers between the top layer and the bottom layer are needed to perform intermediate classifications. We maintain a low computational complexity through the use of the layered structure so that each pattern's classification is performed with respect to a small subspace of the whole input space. Our self-organizing neural network will grow as needed (e.g., when more motif patterns are classified). It will give the same amount of attention to each input pattern and will not omit any potential motif patterns. Finally, simulation results show that our algorithm outperforms existing algorithms in certain aspects. In particular, simulation results show that our algorithm can identify motifs with more mutations than existing algorithms. Our algorithm works well for long DNA sequences as well.