Comprehensive vertical sample-based KNN/LSVM classification for gene expression analysis

Journal of Biomedical Informatics
Fei PanWilliam Perrizo


Classification analysis of microarray gene expression data has been widely used to uncover biological features and to distinguish closely related cell types that often appear in the diagnosis of cancer. However, the number of dimensions of gene expression data is often very high, e.g., in the hundreds or thousands. Accurate and efficient classification of such high-dimensional data remains a contemporary challenge. In this paper, we propose a comprehensive vertical sample-based KNN/LSVM classification approach with weights optimized by genetic algorithms for high-dimensional data. Experiments on common gene expression datasets demonstrated that our approach can achieve high accuracy and efficiency at the same time. The improvement of speed is mainly related to the vertical data representation, P-tree,Patents are pending on the P-tree technology. This work is partially supported by GSA Grant ACT#:K96130308. and its optimized logical algebra. The high accuracy is due to the combination of a KNN majority voting approach and a local support vector machine approach that makes optimal decisions at the local level. As a result, our approach could be a powerful tool for high-dimensional gene expression data analysis.


May 2, 2009·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Topon Kumar Paul, Hitoshi Iba
Aug 17, 2005·Artificial Intelligence in Medicine·Jin-Hyuk Hong, Sung-Bae Cho
Apr 10, 2013·Journal of Biomedical Informatics·Zhiyi MaoXueguang Shao
Jul 1, 2008·Journal of Biomedical Informatics·Konstantinos P ExarchosDimitrios I Fotiadis
May 3, 2011·Computer Methods and Programs in Biomedicine·Eunji ShinSanghyun Park
Dec 16, 2016·PloS One·Kouser KAcharya Kshitish K

Related Concepts

Knowledge Representation (Computer)
Computer Assisted Diagnosis
Genetic Screening Method
Malignant Neoplasms
Pattern Recognition System
Signal Processing, Digital
Two-Parameter Models
Cdna Microarrays
MRNA Differential Display

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