2000 SupportVectorMachClassifAndValidOfCancerTis

• (Furey et al., 2000) ⇒ Terrence S. Furey, Nello Cristianini, Nigel Duffy, David W. Bednarski, Michèl Schummer, David Haussler. (2000). “Support Vector Machine Classification and Validation of Cancer Tissue Samples using Microarray Expression Data.” In: Bioinformatics, 16(10) doi:10.1093/bioinformatics/16.10.906.

Subject Headings: Support Vector Machine Classifier.

Quotes

• Motivation: DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data using support vector machines (SVMs). This analysis consists of both classification of the tissue samples, and an exploration of the data for mis-labeled or questionable tissue results.
• Results: We demonstrate the method in detail on samples consisting of ovarian cancer tissues, normal ovarian tissues, and other normal tissues. The dataset consists of expression experiment results for 97802 cDNAs for each tissue. As a result of computational analysis, a tissue sample is discovered and confirmed to be wrongly labeled. Upon correction of this mistake and the removal of an outlier, perfect classification of tissues is achieved, but not with high confidence. We identify and analyse a subset of genes from the ovarian dataset whose expression is highly differentiated between the types of tissues. To show robustness of the SVM method, two previously published datasets from other types of tissues or cells are analysed. The results are comparable to those previously obtained. We show that other machine learning methods also perform comparably to the SVM on many of those datasets.

Introduction

• …
• Support vector machines (SVMs), a supervised machine learning technique, have been shown to perform well in multiple areas of biological analysis including evaluation microarray expression data …
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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2000 SupportVectorMachClassifAndValidOfCancerTis	Nigel Duffy David Haussler Nello Cristianini Terrence S. Furey David W. Bednarski Michèl Schummer			Support Vector Machine Classification and Validation of Cancer Tissue Samples using Microarray Expression Data		Bioinformatics Subject Area	http://bioinformatics.oxfordjournals.org/cgi/reprint/16/10/906	10.1093/bioinformatics/16.10.906		2000