Growing Gene Network by Integration of Gene Expression, Motif Sequence and Metabolic Information

In computational biology, gene networks are typically inferred from gene expression data alone. Incorporating multiple types of biological information makes it possible to improve gene network estimation. In this paper, we describe an approach for growing gene network from a sub‐network 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 reconstructing more accurate gene networks.