Earticle

초록

영어

An extremely crucial step in the diagnosis of cancers is to select a small number of informative genes for accurate classification. This issue has become a hot focus in the data mining of gene expression profiles. Especially for data with a large number of cancer types, many conventional classification methods show very poor performance. Here, we proposed a new approach for gene selection and multi-cancer classification based on step-by-step improvement of classification performance (SSiCP). The SSiCP gene selection algorithms were evaluated over the NCI60 and GCM benchmark datasets, with accuracy of 96.6% and 95.5% in 10-fold cross-validation, respectively. Furthermore, the SSiCP outperformed recently published algorithms when applied to another two multi-cancer data sets. Computational evidence indicated that SSiCP can avoid overfitting effectively. Compared with various gene selection algorithms, the implementation of SSiCP is simple and many of the selected genes by SSiCP are shown to be closely related to cancers.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1. Data Sets
  2.2. Gene Pre-selection
  2.3. RFE: Recursive Feature Elimination
  2.4. Feature Selection Methodology
  2.5. Over-fitting Evaluation of SSiCP Algorithm
  2.6. Confirmation of Classification Algorithm in the Second Step of Feature Selection
  2.7. Parameter Selection on Weka
 3. Results
  3.1. Initial Noise Removal and Comparison of Classification Algorithms
  3.2. Gene Selection based on Step-by-step Improvement of Classification Performance
  3.3. Comparison of Computational Results using Four Data Sets
  3.4. Overfitting Evaluation
 4. Discussion
 5. Conclusion
 Acknowledgements
 Disclosure
 References

키워드

저자

• Xiaobo Li [ Department of Computer Science and Technology, College of Engineering, Lishui University, Lishui 323000, China ]
• Xue Gong [ Department of Microbiology and Immunology, School of Medicine, Stanford University, Stanford, CA 94305-5101, USA ]
• Xiaoning Peng [ Department of Internal Medicine, School of Medicine, Hunan Normal University, ChangSha 410006, China ]
• Sihua Peng [ Department of Biological Technology, School of Fisheries and Life Science, Shanghai Ocean University, Shanghai 201306, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605