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Semi-Supervised Learning$
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Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien

Print publication date: 2006

Print ISBN-13: 9780262033589

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262033589.001.0001

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Semi-Supervised Protein Classification Using Cluster Kernels

Semi-Supervised Protein Classification Using Cluster Kernels

(p.342) (p.343) 19 Semi-Supervised Protein Classification Using Cluster Kernels
Semi-Supervised Learning

Weston Jason

Leslie Christina

Ie Eugene

Noble William Stafford

The MIT Press

This chapter describes an experimental study of large-scale semi-supervised learning for the problem of protein classification. The protein classification problem, a central problem in computational biology, is to predict the structural class of a protein given its amino acid sequence. Such a classification helps biologists to understand the function of a protein. Building an accurate protein classification system, as with many tasks, depends critically upon choosing a good representation of the input sequences of amino acids. Early work using string kernels with support vector machines (SVMs) for protein classification achieved state-of-the-art classification performance. However, such representations are based only on labeled data—examples with known three-dimensional (3D) structures, organized into structural classes-while in practice, unlabeled data are far more plentiful.

Keywords:   semi-supervised learning, protein classification problem, computational biology, amino acid sequence, input sequences, string kernels, support vector machines, SVMs, labeled data, unlabeled data

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