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Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites.

Lin S, Song Q, Tao H, Wang W, Wan W, Huang J, Xu C, Chebii V, Kitony J, Que S, Harrison A, He H - Sci Rep (2015)

Bottom Line: Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site.A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms.Rice_Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC_Os03g51600.1, a protein sequence which did not appear in the training dataset.

View Article: PubMed Central - PubMed

Affiliation: College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.

ABSTRACT
Experimentally-determined or computationally-predicted protein phosphorylation sites for distinctive species are becoming increasingly common. In this paper, we compare the predictive performance of a novel classification algorithm with different encoding schemes to develop a rice-specific protein phosphorylation site predictor. Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site. A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms. We have used SVM with AF-CKSAAP to construct a rice-specific protein phosphorylation sites predictor, Rice_Phospho 1.0 (http://bioinformatics.fafu.edu.cn/rice_phospho1.0). We measure the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) of Rice_Phospho 1.0 to be 82.0% and 0.64, significantly higher than those measures for other predictors such as Scansite, Musite, PlantPhos and PhosphoRice. Rice_Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC_Os03g51600.1, a protein sequence which did not appear in the training dataset. In summary, Rice_phospho 1.0 outputs reliable predictions of protein phosphorylation sites in rice, and will serve as a useful tool to the community.

No MeSH data available.


MCC of predicting performance of different classification algorithms with different encoding schemes.
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f3: MCC of predicting performance of different classification algorithms with different encoding schemes.

Mentions: The performance of the classifiers with the six different encoding schemes were firstly compared. The best results of a DT classifier were for CKSAAP, with an ACC of 71.14% and MCC of 0.314 (Fig. 3). The best results of a KNN classifier were for CKSAAP-KNN, with an ACC of 73.71% and MCC of 0.402 (Fig. 3). The best results of a RF classifier were for AF-KNN, with an ACC of 75.1% and MCC of 0.458 (Fig. 3). The best results of a SVM classifier were for AF-CKSAAP, with an ACC of 80.90% and MCC of 0.617 (Fig. 3).


Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites.

Lin S, Song Q, Tao H, Wang W, Wan W, Huang J, Xu C, Chebii V, Kitony J, Que S, Harrison A, He H - Sci Rep (2015)

MCC of predicting performance of different classification algorithms with different encoding schemes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493637&req=5

f3: MCC of predicting performance of different classification algorithms with different encoding schemes.
Mentions: The performance of the classifiers with the six different encoding schemes were firstly compared. The best results of a DT classifier were for CKSAAP, with an ACC of 71.14% and MCC of 0.314 (Fig. 3). The best results of a KNN classifier were for CKSAAP-KNN, with an ACC of 73.71% and MCC of 0.402 (Fig. 3). The best results of a RF classifier were for AF-KNN, with an ACC of 75.1% and MCC of 0.458 (Fig. 3). The best results of a SVM classifier were for AF-CKSAAP, with an ACC of 80.90% and MCC of 0.617 (Fig. 3).

Bottom Line: Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site.A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms.Rice_Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC_Os03g51600.1, a protein sequence which did not appear in the training dataset.

View Article: PubMed Central - PubMed

Affiliation: College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.

ABSTRACT
Experimentally-determined or computationally-predicted protein phosphorylation sites for distinctive species are becoming increasingly common. In this paper, we compare the predictive performance of a novel classification algorithm with different encoding schemes to develop a rice-specific protein phosphorylation site predictor. Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site. A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms. We have used SVM with AF-CKSAAP to construct a rice-specific protein phosphorylation sites predictor, Rice_Phospho 1.0 (http://bioinformatics.fafu.edu.cn/rice_phospho1.0). We measure the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) of Rice_Phospho 1.0 to be 82.0% and 0.64, significantly higher than those measures for other predictors such as Scansite, Musite, PlantPhos and PhosphoRice. Rice_Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC_Os03g51600.1, a protein sequence which did not appear in the training dataset. In summary, Rice_phospho 1.0 outputs reliable predictions of protein phosphorylation sites in rice, and will serve as a useful tool to the community.

No MeSH data available.