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The spectrum kernel: a string kernel for SVM protein classification.

Christina Leslie¹, Eleazar Eskin, William Stafford Noble

¹Department of Computer Science, Columbia University, New York, NY 10027, USA. cleslie.noble@cs.columbia.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|April 4, 2002

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We developed a new spectrum kernel for protein classification using support vector machines (SVMs). This method efficiently detects protein homology and classifies sequences in linear time.

Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Protein classification is crucial for understanding protein function and evolution.
Existing homology detection methods can be computationally intensive.
Support Vector Machines (SVMs) are powerful tools for classification tasks.

Purpose of the Study:

Introduce a novel sequence-similarity kernel, the spectrum kernel.
Evaluate its performance for protein classification and homology detection.
Develop a computationally efficient classification method.

Main Methods:

Implemented the spectrum kernel for use with SVMs.
Utilized a discriminative approach for protein classification.
Tested the method on the Structural Classification of Proteins (SCOP) database.

Related Experiment Videos

Main Results:

The spectrum kernel demonstrated strong performance in homology detection.
Achieved comparable results to state-of-the-art methods.
The resulting SVM classifier enabled linear time classification of test sequences.

Conclusions:

String-based kernels, like the spectrum kernel, offer a viable alternative for protein classification.
SVMs combined with spectrum kernels provide a computationally efficient approach.
This method advances discriminative approaches in bioinformatics.