Kernel-based distance metric learning for microarray data classification.

Full Abstract

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.

CONCLUSION:
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.

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Author information

Author/s: Xiong, Huilin (H); Chen, Xue-wen (XW);

Affiliation: 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(-atsign-)ittc.ku.edu

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.

Journal: BMC bioinformatics (BMC Bioinformatics), published in England. (Language: eng)

Reference: 2006-; vol 7 (issue ) : pp 299

Dates: Created 2006/07/20; Completed 2006/08/09; Revised 2008/11/20;

PMID: 16774678, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Artificial Intelligence
Databases, Protein
Diagnosis, Computer-Assisted - methods
Discriminant Analysis
Gene Expression Profiling - methods
Humans

Neoplasm Proteins - metabolism
Neoplasms - diagnosis; genetics; metabolism
Oligonucleotide Array Sequence Analysis - methods
Pattern Recognition, Automated - methods
Tissue Array Analysis - methods
Tumor Markers, Biological - metabolism

Associated Chemicals: Neoplasm Proteins (0) ; Tumor Markers, Biological (0)

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