Growing enzyme gene networks by integration of gene expression, motif sequence, and metabolic information

Authors:
Bo Geng;Xiaobo Zhou;Y. S. Hung
Affiliations:
Bioinformatics Core and Department of Radiology, The Methodist Hospital Research Institute and Cornell University, Houston, TX 77030, USA and Department of Electrical and Electronic Engineering, U ...;Bioinformatics Core and Department of Radiology, The Methodist Hospital Research Institute and Cornell University, Houston, TX 77030, USA;Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong
Venue:
Pattern Recognition
Year:
2009

Citing 1
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Abstract

In computational biology, gene networks are typically inferred from gene expression data alone. Incorporating multiple types of biological evidences makes it possible to improve gene network estimation. In this paper, we describe an approach for building enzyme gene networks by the integration of gene expression data, motif sequence, and metabolic information. To evaluate the approach, we apply it to a pool of E. coli genes related to aspartate pathway. The results show that integrative approach has potentials of obtaining more accurate gene networks.