Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Determination of chemo-responses for osteosarcoma using a hybrid evolutionary algorithm

    153967_153967.pdf (1.817Mb)
    Access Status
    Open access
    Authors
    Chan, Kit Yan
    Zhu, H.
    Lau, C.
    Dillon, Tharam S.
    Ling, S.
    Date
    2010
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Chan, Kit Yan and Zhu, Hailong and Lau, Ching and Dillon, Tharam S. and Ling, Sai Ho. 2010. Determination of chemo-responses for osteosarcoma using a hybrid evolutionary algorithm, in Fogel, G. (ed), IEEE Congress on Evolutionary Computation (CEC 2010), Jul 18 2010, pp. 1-5. Barcelona, Spain: IEEE.
    Source Title
    Proceedings of the IEEE congress on evolutionary computation (CEC 2010)
    Source Conference
    IEEE Congress on Evolutionary Computation (CEC 2010)
    DOI
    10.1109/CEC.2010.5586308
    ISBN
    9781424469093
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    Remarks

    Copyright © 2010 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/3481
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, a hybrid evolutionary algorithm (HEA) based on the approaches of the evolutionary algorithm and a local search (LS) is proposed to determine the gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients, which is one of the most common malignant bone tumor in children. The HEA consists of a population of individuals but the evolution of individuals is conducted by a LS, rather than the crossover and mutation used in the traditional evolutionary algorithms. The proposed HEA can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. Experimental results indicate that HEA can obtain more accurate signatures than the other existing approaches in determining chemoresponse for osteosarcoma.

    Related items

    Showing items related by title, author, creator and subject.

    • An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma
      Chan, Kit Yan; Zhu, H.; Aydin, M.; Lau, C. (2010)
      In genomic studies of cancers, identification of genetic biomarkers from analyzing microarray chip that interrogate thousands of genes is important for diagnosis and therapeutics. However, the commonly used statistical ...
    • Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function
      Lu, H.; Sriyanyong, P.; Song, Y.; Dillon, Tharam S. (2010)
      Particle swarm optimization (PSO) is a population-based evolutionary technique. Advancements in the PSO development over the last decade have made it one of the most promising optimization algorithms for a wide range of ...
    • Space-Time Repetitive Project Scheduling Considering Location and Congestion
      Tao, S.; Wu, Changzhi; Sheng, Z.; Wang, Xiangyu (2018)
      © 2018 American Society of Civil Engineers. Repetitive projects account for a large proportion of construction projects. Different from standard project scheduling problems, scheduling such projects requires considering ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.