HMM Approach for Classifying Protein Structures

Authors:
Georgina Mirceva;Danco Davcev
Affiliations:
Faculty of electrical engineering and information technologies, Univ. Ss. Cyril and Methodius, Skopje, Macedonia;Faculty of electrical engineering and information technologies, Univ. Ss. Cyril and Methodius, Skopje, Macedonia
Venue:
FGIT '09 Proceedings of the 1st International Conference on Future Generation Information Technology
Year:
2009

Citing 5
Cited 0

Support-Vector Networks

Machine Learning
Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Protein Classification Using Consensus Decision

CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Pattern recognition methods for advanced stochastic protein sequence analysis using HMMs

Pattern Recognition
Hidden Markov processes

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

To understand the structure-to-function relationship, life sciences researchers and biologists need to retrieve similar structures from protein databases and classify them into the same protein fold. With the technology innovation the number of protein structures increases every day, so, retrieving structurally similar proteins using current structural alignment algorithms may take hours or even days. Therefore, improving the efficiency of protein structure retrieval and classification becomes an important research issue. In this paper we propose novel approach which provides faster classification (minutes) of protein structures. We build separate Hidden Markov Model for each class. In our approach we align tertiary structures of proteins. Additionally we have compared our approach against an existing approach named 3D HMM. The results show that our approach is more accurate than 3D HMM.