Weston Jason, Leslie Christina, Ie Eugene, and Noble William Stafford
- Published in print:
- 2006
- Published Online:
- August 2013
- ISBN:
- 9780262033589
- eISBN:
- 9780262255899
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262033589.003.0019
- Subject:
- Computer Science, Machine Learning
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 ...
More
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.Less
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.
