Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5615
Title: Hierarchical motif vectors for prediction of functional sites in amino acid sequences using quasi-supervised learning
Authors: Karaçalı, Bilge
Keywords: Functional attribute prediction
Hierarchical motif vectors
Protein sequence analysis
Forecasting
Learning frameworks
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Karaçalı, B. (2012). Hierarchical motif vectors for prediction of functional sites in amino acid sequences using quasi-supervised learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(5), 1432-1441. doi:10.1109/TCBB.2012.68
Abstract: We propose hierarchical motif vectors to represent local amino acid sequence configurations for predicting the functional attributes of amino acid sites on a global scale in a quasi-supervised learning framework. The motif vectors are constructed via wavelet decomposition on the variations of physico-chemical amino acid properties along the sequences. We then formulate a prediction scheme for the functional attributes of amino acid sites in terms of the respective motif vectors using the quasi-supervised learning algorithm that carries out predictions for all sites in consideration using only the experimentally verified sites. We have carried out comparative performance evaluation of the proposed method on the prediction of N-glycosylation of 55,184 sites possessing the consensus N-glycosylation sequon identified over 15,104 human proteins, out of which only 1,939 were experimentally verified N-glycosylation sites. In the experiments, the proposed method achieved better predictive performance than the alternative strategies from the literature. In addition, the predicted N-glycosylation sites showed good agreement with existing potential annotations, while the novel predictions belonged to proteins known to be modified by glycosylation.
URI: http://doi.org/10.1109/TCBB.2012.68
http://hdl.handle.net/11147/5615
ISSN: 1545-5963
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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