Identification of motifs with insertions and deletions in protein sequences using self-organizing neural networks

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 provid...

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Bibliographic Details
Published in:Neural networks 2005-07, Vol.18 (5), p.835-842
Main Authors: Liu, Derong, Xiong, Xiaoxu, Hou, Zeng-Guang, DasGupta, Bhaskar
Format: Article
Language:English
Subjects:
Algorithms
Amino Acid Motifs
Amino Acid Sequence
Applied sciences
Artificial intelligence
Classification
Computer science
control theory
systems
Connectionism. Neural networks
DNA
DNA Transposable Elements
Exact sciences and technology
Gene Deletion
Molecular Sequence Data
Motif identification
Multiple sequence alignment
Neural network
Neural Networks (Computer)
Self-organization
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Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2005.06.007