TY - GEN
T1 - Bi-Phase evolutionary biclustering algorithm with the NSGA-II algorithm
AU - Kong, Zhoufan
AU - Huang, Qinghua
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The gene expression data analysis is significant in investigating the fundamental biological phenomena. Biclustering algorithm is one of the powerful tools to discover the consistent patterns, and has commonly been utilized in the analysis of gene expression data. In this paper, we introduce an innovative biclustering algorithm which incorporates a bi-phase evolutionary architecture and the Non-dominated sorting and sharing (NSGA-II) algorithm. The first phase of the evolution is designed for the population of columns and rows, the second phase of evolution is for the population of biclusters. The two populations are initialized by a hierarchical clustering (HC) algorithm, and then the two populations are treated as two independent population to evolve in two phase respectively. The proposed algorithm was implemented both on synthetic datasets and real datasets, comparative experiments between the proposed algorithm and several typical algorithms demonstrate the effectiveness of the proposed algorithm.
AB - The gene expression data analysis is significant in investigating the fundamental biological phenomena. Biclustering algorithm is one of the powerful tools to discover the consistent patterns, and has commonly been utilized in the analysis of gene expression data. In this paper, we introduce an innovative biclustering algorithm which incorporates a bi-phase evolutionary architecture and the Non-dominated sorting and sharing (NSGA-II) algorithm. The first phase of the evolution is designed for the population of columns and rows, the second phase of evolution is for the population of biclusters. The two populations are initialized by a hierarchical clustering (HC) algorithm, and then the two populations are treated as two independent population to evolve in two phase respectively. The proposed algorithm was implemented both on synthetic datasets and real datasets, comparative experiments between the proposed algorithm and several typical algorithms demonstrate the effectiveness of the proposed algorithm.
KW - Biclustering detection
KW - Evolutionary algorithm
KW - Multi-objective algorithm
UR - http://www.scopus.com/inward/record.url?scp=85073198898&partnerID=8YFLogxK
U2 - 10.1109/ICARM.2019.8834068
DO - 10.1109/ICARM.2019.8834068
M3 - 会议稿件
AN - SCOPUS:85073198898
T3 - 2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
SP - 146
EP - 149
BT - 2019 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019
Y2 - 3 July 2019 through 5 July 2019
ER -