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This article is part of the supplement: Proceedings of the 2009 AMIA Summit on Translational Bioinformatics

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Analysis of AML genes in dysregulated molecular networks

Eunjung Lee12, Hyunchul Jung1, Predrag Radivojac3, Jong-Won Kim4 and Doheon Lee1*

Author Affiliations

1 Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea

2 Biomedical Research Center, KAIST, Daejeon 305-701, South Korea

3 School of Informatics, Indiana University, Bloomington, IN 47408, USA

4 Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul 135-710, South Korea

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BMC Bioinformatics 2009, 10(Suppl 9):S2  doi:10.1186/1471-2105-10-S9-S2

Published: 17 September 2009



Identifying disease causing genes and understanding their molecular mechanisms are essential to developing effective therapeutics. Thus, several computational methods have been proposed to prioritize candidate disease genes by integrating different data types, including sequence information, biomedical literature, and pathway information. Recently, molecular interaction networks have been incorporated to predict disease genes, but most of those methods do not utilize invaluable disease-specific information available in mRNA expression profiles of patient samples.


Through the integration of protein-protein interaction networks and gene expression profiles of acute myeloid leukemia (AML) patients, we identified subnetworks of interacting proteins dysregulated in AML and characterized known mutation genes causally implicated to AML embedded in the subnetworks. The analysis shows that the set of extracted subnetworks is a reservoir rich in AML genes reflecting key leukemogenic processes such as myeloid differentiation.


We showed that the integrative approach both utilizing gene expression profiles and molecular networks could identify AML causing genes most of which were not detectable with gene expression analysis alone due to the minor changes in mRNA level.