MESSM: a framework for protein fold recognition using neural networks and support vector machines.
A new framework (called MESSM) for protein fold recognition with three key features is proposed in this paper. Being tested on three benchmark problems, the results show that the MESSM has a comparable performance on fold recognition to those more computational intensive, energy potential based fold recognition models. The MESSM leads to a better performance on alignment accuracy. The MESSM presents a new way to develop an efficient tool for protein fold recognition.
Jiang
Nan
N
School of Computing Science, Middlesex University, UK. j.nan@mdx.ac.uk
Wu
Wendy
W
Mitchell
Ian
I
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D015233:Models, Statistical; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D009474:Neurons / Q000378:metabolism; D010363:Pattern Recognition, Automated; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012997:Solvents / Q000737:chemistry
D011506:Proteins; D012997:Solvents