TY  - JOUR
AU  - GUBBI, J
AU  - LAI, DTH
AU  - PALANISWAMI, M
AU  - PARKER, M
Y2  - 2014/05/21
Y1  - 2006/12
SN  - 1469-0268
UR  - http://hdl.handle.net/11343/29290
AB  - <jats:p> Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilities for multi-class classification. The effect of using Chou–Fasman parameters and physico-chemical parameters along with evolutionary information in the form of position specific scoring matrix (PSSM) is analyzed. These proposed methods are tested on the RS126 and CB513 datasets. A new dataset is curated (PSS504) using recent release of CATH. On the CB513 dataset, sevenfold cross-validation accuracy of 77.9% was obtained using the proposed encoding method. A new method of calculating the reliability index based on the number of votes and the Support Vector Machine decision value is also proposed. A blind test on the EVA dataset gives an average Q<jats:sub>3</jats:sub> accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on . </jats:p>
LA  - en
PB  - World Scientific Pub Co Pte Lt
KW  - Artificial Intelligence and Image Processing
T1  - PROTEIN SECONDARY STRUCTURE PREDICTION USING SUPPORT VECTOR MACHINES AND A NEW FEATURE REPRESENTATION
DO  - 10.1142/s1469026806002076
IS  - International Journal of Computational Intelligence and Applications
VL  - 06
IS  - 04
SP  - 551-567
L1  - /bitstream/handle/11343/29290/277998_81511.pdf?sequence=1&isAllowed=n
ER  -