Paper Info
Title
Prediction of protein structures using a map-reduce Hadoop framework based simulated annealing algorithm
Abstract
The accomplishment of molecular functions depends on protein tertiary structures. The development of protein structure prediction algorithms and tools is essential for proteomics study. Among the existing developed prediction algorithms, simulated annealing (SA) is extensively used to predict protein structures. However, SA has the incent disadvantages of computing time consuming and local minimum convergence problem. With the application of the cloud computing technique such as Apache Hadoop in bioinformatics research area, we combined SA algorithms to predict protein structures onto the Hadoop parallel computing platform. We applied this platform to predict the protein structures for a public protein dataset. The experimental results show that our platform provides a better and feasible solution for the protein structure prediction compared with an individual computation node.
Year
DOI
Venue
2013
10.1109/BIBM.2013.6732710
BIBM
Keywords
Field
DocType
local minimum convergence problem,map-reduce hadoop framework based simulated annealing algorithm,simulated annealing,apache hadoop,protein structure prediction algorithms,proteins,molecular functions,hadoop parallel computing platform,protein tertiary structures,molecular biophysics,cloud computing technique,public protein dataset,proteomics,sa algorithms,map-reduce,bioinformatics,bioinformatics research area,cloud computing
Protein structure prediction,Data mining,Computer science,Prediction algorithms,Artificial intelligence,Computation,Protein structure,Simulated annealing,Convergence problem,Molecular biophysics,Bioinformatics,Machine learning,Cloud computing
Conference
ISSN
Citations 
PageRank 
2156-1125
0
0.34
References 
Authors
6
2
Name
Order
Citations
PageRank
Hui Li135.46
Chun-Mei Liu224541.30