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The context-tree kernel for strings.

Marco Cuturi¹, Jean-Philippe Vert

¹Computational Biology Group, Ecole des Mines de Paris, 35 rue Saint Honoré, 77300 Fontainebleau, France. marco.cuturi@ensmp.fr

Neural Networks : the Official Journal of the International Neural Network Society

|October 4, 2005

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We developed a novel string kernel using information theory and data compression. This method efficiently classifies protein sequences, outperforming existing techniques without requiring biological expertise.

Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

String kernels are crucial for machine learning tasks like sequence classification.
Existing methods may require extensive biological prior knowledge or computational resources.

Purpose of the Study:

To introduce a new string kernel leveraging information theory and data compression.
To enable efficient and accurate string classification, particularly in proteomics.
To demonstrate the kernel's effectiveness without relying on biological domain expertise.

Main Methods:

Developed a novel kernel for strings inspired by information theory and data compression.
Utilized a Bayesian averaging framework with conjugate priors on probabilistic suffix trees (context-trees).

Related Experiment Videos

Adapted the context-tree weighting algorithm for efficient kernel computation in linear time and space.

Main Results:

The proposed kernel was computed in linear time and space.
Demonstrated strong performance on a standard protein homology detection experiment.
Achieved competitive results compared to state-of-the-art methods.

Conclusions:

The context-tree kernel offers an efficient and effective approach for string classification.
This method shows promise for applications in proteomics and other sequence analysis domains.
The kernel's ability to perform well without biological priors is a significant advantage.