Document Detail


Identification of motifs with insertions and deletions in protein sequences using self-organizing neural networks.
MedLine Citation:
PMID:  16109474     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Derong Liu; Xiaoxu Xiong; Zeng-Guang Hou; Bhaskar Dasgupta
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Neural networks : the official journal of the International Neural Network Society     Volume:  18     ISSN:  0893-6080     ISO Abbreviation:  Neural Netw     Publication Date:    2005 Jun-Jul
Date Detail:
Created Date:  2005-09-05     Completed Date:  2005-11-09     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  8805018     Medline TA:  Neural Netw     Country:  United States    
Other Details:
Languages:  eng     Pagination:  835-42     Citation Subset:  IM    
Affiliation:
Department of Electrical and Computer Engineering, University of Illinois, Chicago, IL 60607, USA. dliu@ece.uic.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Amino Acid Motifs*
Amino Acid Sequence
Classification
DNA Transposable Elements*
Gene Deletion*
Molecular Sequence Data
Neural Networks (Computer)*
Chemical
Reg. No./Substance:
0/DNA Transposable Elements

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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