Supervised classification of protein structures based on convex hull representation.
One of the central problems in functional genomics is to establish the classification schemes of protein structures. In this paper the relationship of protein structures is uncovered within the framework of supervised learning. Specifically, the novel patterns based on convex hull representation are firstly extracted from a protein structure, then the classification system is constructed and machine learning methods such as neural networks, Hidden Markov Models (HMM) and Support Vector Machines (SVMs) are applied. The CATH scheme is highlighted in the classification experiments. The results indicate that the proposed supervised classification scheme is effective and efficient.
Academy of Mathematics and Systems Science, CAS, Beijing 100080, China. ywang@amss.ac.cn
Yong
Y
Wang
Ling-Yun
LY
Wu
Luonan
L
Chen
Xiang-Sun
XS
Zhang
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D023281:Genomics; D008433:Mathematics; D008958:Models, Molecular; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000145:classification
D011506:Proteins