Abstract

This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

Highlights

  • IntroductionCancer has been one of the most common lethal factors for human beings. Missed and mistaken diagnosis sometimes makes people lose the best chance for appropriate treatments

  • Nowadays, cancer has been one of the most common lethal factors for human beings

  • binary version of QPSO (BQPSO)/support vector machine (SVM) approach was implemented on MATLAB, along with binary particle swarm optimization (BPSO)/SVM and genetic algorithm coupling SVM (GA/SVM)

Read more

Summary

Introduction

Cancer has been one of the most common lethal factors for human beings. Missed and mistaken diagnosis sometimes makes people lose the best chance for appropriate treatments. More auxiliary measurements are needed to promote the accuracy of cancer diagnosis and clinical test combined with medical ways [1,2,3,4]. With the rapid development of information sciences and molecular biological sciences, gene microarray technology brings people large amount of high-throughput gene profiles which are widely used in cancer diagnosis, clinical inspection, and other aspects. Effective methods of selecting feature genes for cancer are critically necessary. These methods should be able to robustly identify a subset of informative genes embedded out of a large data set which is contaminated with high dimensional noise

Results
Discussion
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.