Application of a Genetic Algorithm/K-Nearest Neighbor Method to the Classification of Renal Cell Carcinoma

Authors:
Dongqing Liu;Ting Shi;Joseph A. DiDonato;John D. Carpten;Jianping Zhu;Zhong-Hui Duan
Affiliations:
University of Akron;Cleveland Clinic Foundation;Cleveland Clinic Foundation;Translational Genomics Research Institute;University of Akron;University of Akron
Venue:
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Year:
2004

Citing 1
Cited 1

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, we use a genetic algorithm and k-nearest neighbor method to classify two subtypes of renal cell carcinoma using a set of microarray gene expression profiles of nine samples (three clear cell tumors and six papillary tumors). We show that the genetic algorithm/k-nearest neighbor method can be efficiently used in identifying a panel of discriminator genes. To test the robustness of the algorithm, we perform a bootstrapping analysis that removes one sample from the data set at a time and uses the remaining samples for gene selection. We show that each of the removed samples can be classified correctly. We also analyze the stability of the algorithm and the sensitivity of the algorithm with respect to different samples.