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Updated: Jun 27, 2026

Chromatin Immunoprecipitation of Murine Brown Adipose Tissue

Chromatin Immunoprecipitation of Murine Brown Adipose Tissue

Published on: November 21, 2018

Marginalized kernels for biological sequences.

Koji Tsuda¹, Taishin Kin, Kiyoshi Asai

¹Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6 Aomi Koto-ku, Tokyo 135-0064, Japan. koji.tsuda@aist.go.jp

Bioinformatics (Oxford, England)

|August 10, 2002

Summary

This summary is machine-generated.

We introduce a novel method for designing kernels using latent variable models, applicable to various data types. This approach generalizes the Fisher kernel and proves effective for biological sequence classification.

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Area of Science:

Machine Learning
Computational Biology
Statistical Modeling

Background:

Kernel methods, like Support Vector Machines, necessitate predefined kernel functions.
Existing methods for deriving kernels from probability distributions, such as the Fisher kernel, lack a general design methodology.

Purpose of the Study:

To propose a general methodology for designing kernels, particularly for data generated from latent variable models.
To extend kernel design principles to incorporate hidden variables within probabilistic models.

Main Methods:

Design a joint kernel for complete data (visible and hidden variables) within latent variable models.
Derive a marginalized kernel for visible data by integrating out hidden variables.
Demonstrate that the Fisher kernel is a specific instance of these marginalized kernels.

Main Results:

Developed a general framework for constructing marginalized kernels from latent variable models.
Derived novel marginalized kernels applicable to biological sequences like DNA and proteins.
Showcased the Fisher kernel as a special case of marginalized kernels, offering new theoretical insights.

Conclusions:

The proposed marginalized kernel approach provides a flexible and powerful tool for kernel design.
This methodology offers a unified perspective on kernel theory and its relation to latent variable models.
Marginalized kernels demonstrate significant effectiveness in biological sequence classification tasks, such as identifying bacterial gyrase subunit B (gyrB) sequences.