Prediction of protein structural class with Rough Sets.
BACKGROUND
A new method for the prediction of protein structural classes is constructed based on Rough Sets algorithm, which is a rule-based data mining method. Amino acid compositions and 8 physicochemical properties data are used as conditional attributes for the construction of decision system. After reducing the decision system, decision rules are generated, which can be used to classify new objects.


RESULTS
In this study, self-consistency and jackknife tests on the datasets constructed by G.P. Zhou (Journal of Protein Chemistry, 1998, 17: 729-738) are used to verify the performance of this method, and are compared with some of prior works. The results showed that the rough sets approach is very promising and may play a complementary role to the existing powerful approaches, such as the component-coupled, neural network, SVM, and LogitBoost approaches.


CONCLUSIONS
The results with high success rates indicate that the rough sets approach as proposed in this paper might hold a high potential to become a useful tool in bioinformatics.
Cao
Youfang
Y
Plant Biotechnology Research Center, Fudan-SJTU-Nottingham Plant Biotechnology R&D Center, School of Agriculture and Biology, Institute of Systems Biology, Shanghai Jiao Tong University, Shanghai 200030, China. yfcao@sjtu.edu.cn
Liu
Shi
S
Zhang
Lida
L
Qin
Jie
J
Wang
Jiang
J
Tang
Kexuan
K
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
D011506:Proteins