TY  - JOUR
T1  - Function Based Predictions of Protein Fold Recognition using Go-Term
AU - Loganathan, E. AU - Dinakaran, K., AU - Gnanendra, S. AU - Valarmathie, P. 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 24
SP  - 7534
EP  - 7538
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.7534.7538
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.7534.7538
KW  - Secondary structure
KW  -protein FOLD
KW  -gene ontology
KW  -MeSH terms
KW  -alignment
KW  -development
AB  - Machine learning-based methods are the most prominently employed in methods in the development
of novel protein fold recognition tools. The most recent fold recognition method was developed by combining
the four descriptors (e-Values) of Position Specific Iteration BLAST (PSI BLAST), reverse PSI-BLAST
(RPS-BLAST), alignment of Secondary Structure Elements (SSE) and PROSITE motifs. In this present study,
we emphasized to improve the fold recognition methods by including gene-ontology terms as additional
descriptors which can aid in the determination of function based predictions. This method of descriptor
combinations have resulted high sensitivity in determining the protein folds when compared to the methods
developed with single descriptors. Also, the inclusion of GO-term descriptor have highly increased the
sensitivity of the methods in fold recognition which significantly envisages the usage of GO-terms as prominent
descriptors that can be employed in the protein fold predictions.
ER  -