Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6254
Title: Fast subcellular localization by cascaded fusion of signal-based and homology-based methods
Authors: Mak, MW 
Wang, W
Kung, SY
Issue Date: 14-Oct-2011
Publisher: BioMed Central
Source: Proteome science, 14 Oct. 2011, v. 9, suppl. 1, S8, p.1-12 How to cite?
Journal: Proteome science 
Abstract: Background: The functions of proteins are closely related to their subcellular locations. In the post-genomics era, the amount of gene and protein data grows exponentially, which necessitates the prediction of subcellular localization by computational means.
Results: This paper proposes mitigating the computation burden of alignment-based approaches to subcellular localization prediction by a cascaded fusion of cleavage site prediction and profile alignment. Specifically, the informative segments of protein sequences are identified by a cleavage site predictor using the information in their N-terminal shorting signals. Then, the sequences are truncated at the cleavage site positions, and the shortened sequences are passed to PSI-BLAST for computing their profiles. Subcellular localization are subsequently predicted by a profile-to-profile alignment support-vector-machine (SVM) classifier. To further reduce the training and recognition time of the classifier, the SVM classifier is replaced by a new kernel method based on the perturbational discriminant analysis (PDA).
Conclusions: Experimental results on a new dataset based on Swiss-Prot Release 57.5 show that the method can make use of the best property of signal- and homology-based approaches and can attain an accuracy comparable to that achieved by using full-length sequences. Analysis of profile-alignment score matrices suggest that both profile creation time and profile alignment time can be reduced without significant reduction in subcellular localization accuracy. It was found that PDA enjoys a short training time as compared to the conventional SVM. We advocate that the method will be important for biologists to conduct large-scale protein annotation or for bioinformaticians to perform preliminary investigations on new algorithms that involve pairwise alignments.
URI: http://hdl.handle.net/10397/6254
EISSN: 1477-5956
DOI: 10.1186/1477-5956-9-S1-S8
Rights: © 2011 Mak et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Mak_Subcellular_Localization_Signal-based.pdf1.04 MBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Last Week
0
Last month
0
Citations as of Aug 20, 2017

Page view(s)

87
Last Week
1
Last month
Checked on Aug 20, 2017

Download(s)

55
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.