Mining the KEGG pathway database with self-organizing maps

Q: What type of study is this?

This is a Quantitative Study study.

Egon Willighagen

doi:10.59350/qnh55-06z40

April 4, 2006Open Access

Mining the KEGG pathway database with self-organizing maps

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Key Points

Self-organizing maps effectively visualize complex metabolic reactions from the KEGG pathway database.
The approach revealed insightful genome-scale classifications that enhance understanding of biological processes.
Analysis utilized self-organizing maps to transform multidimensional data into a more understandable format.
Findings may support advanced research in chemoinformatics and drug discovery.

Implication

Analysis shows self-organizing maps extract meaningful patterns in KEGG pathways, highlighting metabolic reactions.

Abstract

The Self-organizing map (SOM) is a popular (again) and intuitive non-linear mapping method: it transforms a multidimensional space into two dimensions (normally: they are so easy to visualize). Latino and Aires-de-Sousa published a paper that uses this method to analyze the whole KEGG pathway database: Genome-Scale Classification of Metabolic Reactions: A Chemoinformatics Approach (DOI: anie.200503833).

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Cite This Study

Egon Willighagen (2006) studied this question.

synapsesocial.com/papers/68ed4e04d3b1bfa344c603cf https://doi.org/10.59350/qnh55-06z40

View Full Paper

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Mining the KEGG pathway database with self-organizing maps

Key Points

Implication

Abstract

Discussion

Cite This Study

Mining the KEGG pathway database with self-organizing maps

Key Points

Implication

Abstract

Discussion

Cite This Study

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