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Related Experiment Video

Updated: Feb 9, 2026

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

The KEGG resource for deciphering the genome.

Minoru Kanehisa¹, Susumu Goto, Shuichi Kawashima

¹Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. kanehisa@kuicr.kyoto-u.ac.jp

Nucleic Acids Research

|December 19, 2003

Summary

Creating a computational cell model is a major post-genomic challenge. KEGG provides integrated knowledge for predicting protein interaction networks from genomic data.

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Area of Science:

Bioinformatics
Systems Biology
Genomics

Background:

The post-genomic era faces the challenge of creating comprehensive computational models of cells and organisms.
Predicting complex cellular processes and organism behavior from genomic information is a key goal.

Purpose of the Study:

To develop a knowledge-based approach for predicting protein interaction networks.
To integrate diverse biological data into a unified framework for network prediction.

Main Methods:

Utilizing the KEGG (Kyoto Encyclopedia of Genes and Genomes) knowledge base.
Integrating pathway, gene, protein, and biochemical compound/reaction databases.
Representing biological information as graph objects: protein network, gene universe, and chemical universe.

Main Results:

KEGG integrates molecular interaction networks, gene/protein information, and biochemical data.
The approach enables prediction of protein interaction networks from genomic data.
Ongoing efforts focus on KEGG Orthology (KO) and the GLYCAN database for enhanced knowledge abstraction.

Conclusions:

A knowledge-based approach using KEGG facilitates computational prediction of cellular networks.
This facilitates understanding higher-level biological complexity from genomic data.
Continued development of KEGG databases enhances the predictive power of computational models.