TY - GEN
T1 - Protein secondary structure prediction via Pigeon-Inspired Optimization
AU - Zheng, Wei
AU - Sun, Hemeng
AU - Duan, Haibin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/20
Y1 - 2017/1/20
N2 - Proteins are the essential elements in all creatures' life process. The prevailing experimental method to detect protein structure is time consuming. According to Anfinsen's theory, the primary structure is the key to shaping the three-dimensional structure of the protein. Thus, the prediction of proteins' structure avoiding complex experiments is theoretically feasible. Algorithms are applied to make the protein structure prediction while they have some unsatisfactory blemish. For instance, relatively high Gibbs free energy or long iterating progress. In this paper, a new algorithm is introduced to solve this problem. Its name is 'Pigeon-Inspired Optimization(PIO) Algorithm'. PIO can work out an accurate prediction in relatively short iterations. The advantages and feasibility of this algorithm will be demonstrated through the experiments, comparing to Particle Swarm Optimization.
AB - Proteins are the essential elements in all creatures' life process. The prevailing experimental method to detect protein structure is time consuming. According to Anfinsen's theory, the primary structure is the key to shaping the three-dimensional structure of the protein. Thus, the prediction of proteins' structure avoiding complex experiments is theoretically feasible. Algorithms are applied to make the protein structure prediction while they have some unsatisfactory blemish. For instance, relatively high Gibbs free energy or long iterating progress. In this paper, a new algorithm is introduced to solve this problem. Its name is 'Pigeon-Inspired Optimization(PIO) Algorithm'. PIO can work out an accurate prediction in relatively short iterations. The advantages and feasibility of this algorithm will be demonstrated through the experiments, comparing to Particle Swarm Optimization.
UR - https://www.scopus.com/pages/publications/85015207044
U2 - 10.1109/CGNCC.2016.7829085
DO - 10.1109/CGNCC.2016.7829085
M3 - 会议稿件
AN - SCOPUS:85015207044
T3 - CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference
SP - 1934
EP - 1938
BT - CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2016
Y2 - 12 August 2016 through 14 August 2016
ER -