What Is A Support Vector Machine

Support vector machines (SVMs) are becoming popular in a wide variety of biological applications. But, what exactly are SVMs and how do they work? And what are their most promising applications in the life sciences?

A support vector machine (SVM) is a computer algorithm that learns by example to assign labels to objects1. For instance, an SVM can learn to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and nonfraudulent credit card activity reports. Alternatively, an SVM can learn to recognize handwritten digits by examining a large collection of scanned images of handwritten zeroes, ones and so forth. SVMs have also been successfully applied to an increasingly wide variety of biological applications. A common biomedical application of support vector machines is the automatic classification of microarray gene expression profiles. Theoretically, an SVM can examine the gene expression profile derived from a tumor sample or from peripheral fluid and arrive at a diagnosis or prognosis. Throughout this primer, I will use as a motivating example a seminal study of acute leukemia expression profiles2. Other biological applications of SVMs involve classifying objects as diverse as protein and DNA sequences, microarray expression profiles and mass spectra3.

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Figure 1: Support vector machines (SVMs) at work. References
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The author thanks the support from NSF award IIS Author information
Authors and Affiliations
1. Departments of Genome Sciences and of Computer Science and Engineering, University of Washington, 1705 NE Pacific Street, Seattle, 98195, Washington, USA William S Noble

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Noble, W. What is a support vector machine?. Nat Biotechnol 24, 1565–1567 (2006). /10.1038/nbt Download citation

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