Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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The problem of motif identification in protein sequences has been studied for many years in the literature. Current popular algorithms of motif identification in protein sequences face two difficulties, high computational cost and the possibility of insertions and deletions. In this paper, we provide a new strategy that solve the problem more efficiently. We develop a self-organizing neural network structure with multiple levels of subnetworks to make an intelligent classification of the subsequences obtained from protein sequences. We maintain a low computational complexity through the use of this multi-level structure so that the classification of each subsequence is performed with respect to a small subspace of the whole input space. The new definition of pairwise distance between motif patterns provided in this paper can deal with up to two insertions/deletions allowed in a motif, while other existing algorithm can only deal with one insertion or deletion. We also maintain a high reliability using our self-organizing neural network since it will grow as needed to make sure all input patterns are considered and are given the same amount of attention. Simulation results show that our algorithm significantly outperforms existing algorithms in both accuracy and reliability aspects.