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Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation
Y Yamanishi1, J-P Vert, A Nakaya
1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. yoshi@kuicr.kyoto-u.ac.jp
Bioinformatics (Oxford, England)
|July 12, 2003
Summary
Investigating correlations between genomic datasets is crucial for pathway reconstruction. Methods were validated by identifying operons in E. coli using functional, geometrical, and co-expression data.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Pathway reconstruction from diverse genomic data (expression, protein interactions, phylogenetic profiles) is a key challenge.
- Assessing data correlations is a critical preliminary step for accurate pathway reconstruction.
Purpose of the Study:
- To evaluate the correlation between different genomic datasets.
- To develop and test methods for pathway reconstruction using integrated genomic data.
Main Methods:
- Comparative analysis of three distinct genomic datasets: functional gene relationships in metabolic pathways, chromosomal geometrical relationships, and gene expression co-expression data.
- Application of developed methods to identify operons within the Escherichia coli genome.
Main Results:
- The study successfully demonstrated the ability of the proposed methods to recognize operons in the E. coli genome.
- Validation was achieved through the integration and comparison of functional, geometrical, and co-expression data.
Conclusions:
- The correlation analysis between diverse genomic datasets is effective for computational biology tasks.
- The validated methods show promise for accurate pathway reconstruction and operon identification.