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• W1567288391 abstract "Current popular algorithms of motif identification in protein sequences face two difficulties, large computation and insertions and deletions of letters. In this paper, we provide a new strategy that solves this problem in a more efficient and effective way. We build a self-organizing neural network with multiple levels of subnetworks to classify subsequences obtained from the 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 more insertions/deletions allowed in a motif than other algorithms. In the simulation result, our algorithm significantly outperforms existing algorithms in both accuracy and reliability aspects." @default.
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• W1567288391 date "2006-01-05" @default.
• W1567288391 modified "2023-09-24" @default.
• W1567288391 title "A self-organizing neural network approach for the identification of motifs with insertions and deletions in protein sequences" @default.
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• W1567288391 doi "https://doi.org/10.1109/ijcnn.2005.1555845" @default.
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