Fuzzy k-nearest neighbor method for protein secondary structure prediction and its parallel implementation

초록

Fuzzy k-nearest neighbor method is a generalization of nearest neighbor method, the simplest algorithm for pattern classification. One of the important areas for application of the pattern classification is the protein secondary structure prediction, an important topic in the field of bioinformatics. In this work, we develop a parallel algorithm for protein secondary structure prediction, based on the fuzzy k-nearest neighbor method, that uses evolutionary profile obtained from PSI-BLAST (Position Specific Iterative Basic Local Sequence Alignment Tool) as the feature vectors.

제목
Fuzzy k-nearest neighbor method for protein secondary structure prediction and its parallel implementation
저자
Kim, Seung-Yeon; Sim, Jaehyun; Lee, Julian
발행일
2006-08
학회명
International Conference on Intelligent Computing, ICIC 2006
개최지
Kunming
개최국가
독일
학회 개최일
2006-08-16 ~ 2006-08-19