Name
Title | ||
|---|---|---|
Protein secondary structure prediction using support vector machine with a PSSM profile and an advanced tertiary classifier |
Abstract | ||
|---|---|---|
In this study, the support vector machine (SVM) is applied as a learning machine for the secondary structure prediction. As an encoding scheme for training the SVM, position-specific scoring matrix (PSSM) is adopted. To improve the prediction accuracy, three optimization processes such as encoding scheme, sliding window size and parameter optimization are performed. For the multi-class classification, the results of three one-versus-one binary classifiers (H/E, E/C and C/H) are combined using our new tertiary classifier called SVM_Represent. By applying this new tertiary classifier, the Q3 prediction accuracy reaches 89.6% on the RSI 26 dataset and 90.1% on the CB513 dataset. Also the Segment Overlap Measure (SOV) is 85.0% on the RS 126 dataset and 85.7% on the CB513 dataset. Compared with the existing best prediction methods, our new prediction algorithm improves the accuracy about 13%) in terms of Q3 and SOV, the two most commonly used accuracy measures. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/CSBW.2005.114 | CSB Workshops |
Keywords | Field | DocType |
optimisation,protein secondary structure prediction,prediction method,prediction accuracy,optimization process,advanced tertiary classifier,encoding scheme,new tertiary classifier,new prediction algorithm,parameter estimation,sliding window size,rs126 dataset,segment overlap measure,cb513 dataset,proteins,learning machine,svm,best prediction method,q3 prediction accuracy,biochemistry,sov,biology computing,molecular biophysics,position-specific scoring matrix,pssm profile,support vector machine,parameter optimization,secondary structure prediction,rs 126 dataset,prediction algorithm,binary classifiers,support vector machines,accuracy measure,multi class classification,sliding window | Learning machine,Data mining,Computer science,Artificial intelligence,Estimation theory,Classifier (linguistics),Binary number,Protein secondary structure prediction,Sliding window protocol,Pattern recognition,Support vector machine,Machine learning,Encoding (memory) | Conference |
ISBN | Citations | PageRank |
0-7695-2442-7 | 1 | 0.43 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hae-Jin Hu | 1 | 24 | 4.47 |
| Phang C. Tai | 2 | 102 | 11.10 |
| Robert Harrison | 3 | 61 | 5.62 |
| Jieyue He | 4 | 128 | 18.92 |
| Yi Pan | 5 | 2507 | 203.23 |