Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6866174 | Neurocomputing | 2015 | 11 Pages |
Abstract
Correct prediction of protein secondary structural classes is vital for the prediction of tertiary structures and understanding of their function. Most of the prediction algorithms require lengthy computation time. Nearest neighbor - complexity distance measure (NN-CDM) algorithm was one of the significant prediction algorithms using Lempel-Ziv (LZ) complexity-based distance measure, but it is slow and ineffective in handling uncertainties. To solve the problems, we propose fuzzy NN-CDM (FKNN-CDM) algorithm that incorporates the confidence level of prediction results and enhance the prediction process by designing hardware architecture that implements the proposed algorithm in an FPGA board. Highest average prediction accuracies for Z277 and 25PDB datasets using proposed algorithm are 84.12% and 47.81% respectively, with 15 times faster computation using an Altera DE2-115 FPGA board.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yong Tat Tan, Bakhtiar Affendi Rosdi,