Cancer Diagnosis and Protein Secondary Structure Prediction Using Support Vector Machines

2005 ◽  
pp. 343-363 ◽  
Author(s):  
F. Chu ◽  
G. Jin ◽  
L. Wang
Keyword(s):  
Support Vector Machines ◽  
Secondary Structure ◽  
Cancer Diagnosis ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Support Vector ◽  
Protein Secondary Structure Prediction ◽  
Vector Machines

PROTEIN SECONDARY STRUCTURE PREDICTION USING SUPPORT VECTOR MACHINES AND A NEW FEATURE REPRESENTATION

2006 ◽  
Vol 06 (04) ◽  
pp. 551-567 ◽  
Author(s):  
JAYAVARDHANA GUBBI ◽  
DANIEL T. H. LAI ◽  
MARIMUTHU PALANISWAMI ◽  
MICHAEL PARKER
Keyword(s):  
Support Vector Machines ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Feature Representation ◽  
Evolutionary Information ◽  
Support Vector ◽  
Vector Machines

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 Q3 accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on .

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