Kernel-based distance metric learning for microarray data classification.
BACKGROUND
The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with traditional pattern classifications, gene expression-based data classification is typically characterized by high dimensionality and small sample size, which make the task quite challenging.


RESULTS
In this paper, we present a modified K-nearest-neighbor (KNN) scheme, which is based on learning an adaptive distance metric in the data space, for cancer classification using microarray data. The distance metric, derived from the procedure of a data-dependent kernel optimization, can substantially increase the class separability of the data and, consequently, lead to a significant improvement in the performance of the KNN classifier. Intensive experiments show that the performance of the proposed kernel-based KNN scheme is competitive to those of some sophisticated classifiers such as support vector machines (SVMs) and the uncorrelated linear discriminant analysis (ULDA) in classifying the gene expression data.


CONCLUSIONS
A novel distance metric is developed and incorporated into the KNN scheme for cancer classification. This metric can substantially increase the class separability of the data in the feature space and, hence, lead to a significant improvement in the performance of the KNN classifier.
Xiong
Huilin
H
Bioinformatics and Computational Life Sciences Laboratory, Department of Electrical Engineering and Computer Science, University of Kansas, 2335 Irving Hill Road, Lawrence, Kansas 66045, USA. hlxiong@ittc.ku.edu
Chen
Xue-wen
XW
D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000378:metabolism*; D030562:Databases, Protein; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D016002:Discriminant Analysis; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D046888:Tissue Array Analysis / Q000379:methods*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins