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On Position-Specific Scoring Matrix for Protein Function Prediction
March/April 2011 (vol. 8 no. 2)
pp. 308-315
ASCII Text
x 
 
Jong Cheol Jeong, Xiaotong Lin, Xue-Wen Chen, "On Position-Specific Scoring Matrix for Protein Function Prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 2, pp. 308-315, March/April, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2010.93,
author = {Jong Cheol Jeong and Xiaotong Lin and Xue-Wen Chen},
title = {On Position-Specific Scoring Matrix for Protein Function Prediction},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {8},
number = {2},
issn = {1545-5963},
year = {2011},
pages = {308-315},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.93},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - On Position-Specific Scoring Matrix for Protein Function Prediction
IS - 2
SN - 1545-5963
SP308
EP315
EPD - 308-315
A1 - Jong Cheol Jeong,
A1 - Xiaotong Lin,
A1 - Xue-Wen Chen,
PY - 2011
KW - Clustering
KW - classification
KW - and association rules
KW - data mining
KW - feature extraction or construction
KW - mining methods and algorithms.
VL - 8
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Jong Cheol Jeong, University of Kansas, Lawrence
Xiaotong Lin, The University of Kansas, Lawrence
Xue-Wen Chen, The University of Kansas, Lawrence
While genome sequencing projects have generated tremendous amounts of protein sequence data for a vast number of genomes, substantial portions of most genomes are still unannotated. Despite the success of experimental methods for identifying protein functions, they are often lab intensive and time consuming. Thus, it is only practical to use in silico methods for the genome-wide functional annotations. In this paper, we propose new features extracted from protein sequence only and machine learning-based methods for computational function prediction. These features are derived from a position-specific scoring matrix, which has shown great potential in other bininformatics problems. We evaluate these features using four different classifiers and yeast protein data. Our experimental results show that features derived from the position-specific scoring matrix are appropriate for automatic function annotation.

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Index Terms:
Clustering, classification, and association rules, data mining, feature extraction or construction, mining methods and algorithms.
Citation:
Jong Cheol Jeong, Xiaotong Lin, Xue-Wen Chen, "On Position-Specific Scoring Matrix for Protein Function Prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 2, pp. 308-315, March-April 2011, doi:10.1109/TCBB.2010.93
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