Article citationsMore>>
Bailey, T.L., Boden, M., Buske, F.A., Frith, M., Grant, C.E., Clementi, L., Ren, J., Li, W.W. and Noble, W.S. (2009) MEME SUITE: Tools for motif discovery and searching. Nucleic Acids Research, 37, W202-W208.
http://dx.doi.org/10.1093/nar/gkp335
has been cited by the following article:
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TITLE:
PFP-RFSM: Protein fold prediction by using random forests and sequence motifs
AUTHORS:
Junfei Li, Jigang Wu, Ke Chen
KEYWORDS:
Protein Fold; Structure Analysis; Random Forest; Sequence Motifs
JOURNAL NAME:
Journal of Biomedical Science and Engineering,
Vol.6 No.12,
December
20,
2013
ABSTRACT:
Protein
tertiary structure is indispensible in revealing the biological functions of
proteins. De novo perdition of
protein tertiary structure is dependent on protein fold recognition. This study
proposes a novel method for prediction of protein fold types which takes primary
sequence as input. The proposed method, PFP-RFSM, employs
a random forest classifier and a comprehensive feature representation, including
both sequence and predicted structure descriptors. Particularly, we
propose a method for generation of features based on sequence motifs and those
features are firstly employed in protein fold prediction. PFP-RFSM and ten
representative protein fold predictors are validated in a benchmark dataset
consisting of 27 fold types. Experiments demonstrate that PFP-RFSM outperforms
all existing protein fold predictors and improves the success rates by 2%-14%.
The results suggest sequence motifs are effective in classification and
analysis of protein sequences.
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