TY - GEN
T1 - Exploring potential discriminatory information embedded in PSSM to enhance protein structural class prediction accuracy
AU - Dehzangi, Abdollah
AU - Paliwal, Kuldip
AU - Lyons, James
AU - Sharma, Alok
AU - Sattar, Abdul
PY - 2013
Y1 - 2013
N2 - Determining the structural class of a given protein can provide important information about its functionality and its general tertiary structure. In the last two decades, the protein structural class prediction problem has attracted tremendous attention and its prediction accuracy has been significantly improved. Features extracted from the Position Specific Scoring Matrix (PSSM) have played an important role to achieve this enhancement. However, this information has not been adequately explored since the protein structural class prediction accuracy relying on PSSM for feature extraction still remains limited. In this study, to explore this potential, we propose segmentation-based feature extraction technique based on the concepts of amino acids' distribution and auto covariance. By applying a Support Vector Machine (SVM) to our extracted features, we enhance protein structural class prediction accuracy up to 16% over similar studies found in the literature. We achieve over 90% and 80% prediction accuracies for 25PDB and 1189 benchmarks respectively by solely relying on the PSSM for feature extraction.
AB - Determining the structural class of a given protein can provide important information about its functionality and its general tertiary structure. In the last two decades, the protein structural class prediction problem has attracted tremendous attention and its prediction accuracy has been significantly improved. Features extracted from the Position Specific Scoring Matrix (PSSM) have played an important role to achieve this enhancement. However, this information has not been adequately explored since the protein structural class prediction accuracy relying on PSSM for feature extraction still remains limited. In this study, to explore this potential, we propose segmentation-based feature extraction technique based on the concepts of amino acids' distribution and auto covariance. By applying a Support Vector Machine (SVM) to our extracted features, we enhance protein structural class prediction accuracy up to 16% over similar studies found in the literature. We achieve over 90% and 80% prediction accuracies for 25PDB and 1189 benchmarks respectively by solely relying on the PSSM for feature extraction.
KW - Feature Extraction
KW - Protein Structural Class Prediction Problem
KW - Segmented Auto Covariance
KW - Segmented distribution
KW - Support Vector Machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=84880723300&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880723300&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39159-0_19
DO - 10.1007/978-3-642-39159-0_19
M3 - Conference contribution
AN - SCOPUS:84880723300
SN - 9783642391583
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 208
EP - 219
BT - Pattern Recognition in Bioinformatics - 8th IAPR International Conference, PRIB 2013, Proceedings
PB - Springer Verlag
T2 - 8th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2013
Y2 - 17 June 2013 through 20 June 2013
ER -