Membrane computing to model feature selection of microarray cancer data

Naeimeh Elkhani, Ravie Chandren Muniyandi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cancer is the main public health issue in most places of the world due to the difficulties in its early diagnosis and to begin early treatment. In the recent years many techniques have been proposed to tackle the high dimensionality in cancer datasets. This paper proposed a membrane-inspired feature selection method to utilize the potentials of membrane computing features such as decentralization, non-determinism, and maximal parallel computing for feature selection of cancer data. Kernel p system-one of the variants of membrane computing-is defined based on multi objective binary particle swarm optimization feature selection method through nine steps involving definitions of objects, compartments, rules and output. Matlab software is used to model the proposed approach. The proposed model evaluated by cell line data of breast cancer with six samples of papillary infiltrating ductal carcinoma and Carcinosarcoma disease state. As the initial attempt to come out with a model, division rule and sequential computation on Matlab are used as tools to define potential of membrane computing in trading space against time computation. The evaluation results indicate the proposed model based on kernel p system computation with distributed compartments is capable in distinguishing marker genes.

Original languageEnglish
Title of host publicationACM International Conference Proceeding Series
PublisherAssociation for Computing Machinery
Volume07-09-Ocobert-2015
ISBN (Print)9781450337359
DOIs
Publication statusPublished - 7 Oct 2015
EventASE BigData and SocialInformatics, ASE BD and SI 2015 - Kaohsiung, Taiwan, Province of China
Duration: 7 Oct 20159 Oct 2015

Other

OtherASE BigData and SocialInformatics, ASE BD and SI 2015
CountryTaiwan, Province of China
CityKaohsiung
Period7/10/159/10/15

Fingerprint

Microarrays
Feature extraction
Membranes
Public health
Parallel processing systems
Particle swarm optimization (PSO)
Genes
Cells

Keywords

  • Feature selection
  • Kernel p system
  • Membrane computing
  • Microarray cancer data
  • Particle swarm optimization

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Elkhani, N., & Muniyandi, R. C. (2015). Membrane computing to model feature selection of microarray cancer data. In ACM International Conference Proceeding Series (Vol. 07-09-Ocobert-2015). [a13] Association for Computing Machinery. https://doi.org/10.1145/2818869.2818885

Membrane computing to model feature selection of microarray cancer data. / Elkhani, Naeimeh; Muniyandi, Ravie Chandren.

ACM International Conference Proceeding Series. Vol. 07-09-Ocobert-2015 Association for Computing Machinery, 2015. a13.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Elkhani, N & Muniyandi, RC 2015, Membrane computing to model feature selection of microarray cancer data. in ACM International Conference Proceeding Series. vol. 07-09-Ocobert-2015, a13, Association for Computing Machinery, ASE BigData and SocialInformatics, ASE BD and SI 2015, Kaohsiung, Taiwan, Province of China, 7/10/15. https://doi.org/10.1145/2818869.2818885
Elkhani N, Muniyandi RC. Membrane computing to model feature selection of microarray cancer data. In ACM International Conference Proceeding Series. Vol. 07-09-Ocobert-2015. Association for Computing Machinery. 2015. a13 https://doi.org/10.1145/2818869.2818885
Elkhani, Naeimeh ; Muniyandi, Ravie Chandren. / Membrane computing to model feature selection of microarray cancer data. ACM International Conference Proceeding Series. Vol. 07-09-Ocobert-2015 Association for Computing Machinery, 2015.
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