Exploring and Exploiting Disease Interactions from Multi-Relational Gene and Phenotype Networks
Open Access
- 29 July 2011
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 6 (7), e22670
- https://doi.org/10.1371/journal.pone.0022670
Abstract
The availability of electronic health care records is unlocking the potential for novel studies on understanding and modeling disease co-morbidities based on both phenotypic and genetic data. Moreover, the insurgence of increasingly reliable phenotypic data can aid further studies on investigating the potential genetic links among diseases. The goal is to create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which in turn should generate better data. We build and analyze disease interaction networks based on data collected from previous genetic association studies and patient medical histories, spanning over 12 years, acquired from a regional hospital. By exploring both individual and combined interactions among these two levels of disease data, we provide novel insight into the interplay between genetics and clinical realities. Our results show a marked difference between the well defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. We demonstrate the power of these dependencies by proposing a novel multi-relational link prediction method, showing that disease co-morbidity can enhance our currently limited knowledge of genetic association. Furthermore, our methods for integrated networks of diverse data are widely applicable and can provide novel advances for many problems in systems biology and personalized medicine.Keywords
This publication has 25 references indexed in Scilit:
- Cytoscape 2.8: new features for data integration and network visualizationBioinformatics, 2010
- PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associationsBioinformatics, 2010
- Predicting functionality of protein–DNA interactions by integrating diverse evidenceBioinformatics, 2009
- A Dynamic Network Approach for the Study of Human PhenotypesPLoS Computational Biology, 2009
- Translational disease interpretation with molecular networksGenome Biology, 2009
- The impact of cellular networks on disease comorbidityMolecular Systems Biology, 2009
- Genetics of gene expression and its effect on diseaseNature, 2008
- An integrated approach to inferring gene–disease associations in humansProteins-Structure Function and Bioinformatics, 2008
- The human disease networkProceedings of the National Academy of Sciences of the United States of America, 2007
- Modularity and community structure in networksProceedings of the National Academy of Sciences of the United States of America, 2006