Graph theory-based measures as predictors of gene morbidity
PublisherIEEE Press. Institute of Electrical and Electronics Engineers
Rights accessOpen Access
Previous studies have suggested that some graph properties of protein interaction networks might be related with gene morbidity. In particular, it has been suggested that when a polymorphism affects a gene, it is more likely to produce a disease if the node degree in the interaction network is higher than for other genes. However, these results do not take into account the possible bias introduced by the variance in the amount of information available for different genes. This work models the relationship between the morbidity associated with a gene and the degrees of the nodes in the protein interaction network controlling the amount of information available in the literature. A set of 7461 genes and 3665 disease identifiers reported in the Online Mendelian Inheritance in Man (OMIM) was mined jointly with 9630 nodes and 38756 interactions of the Human Proteome Resource Database (HPRD). The information available from a gene was measured through PubMed mining. Results suggest that the correlation between the degree of a node in the protein interaction network and its morbidity is largely contributed by the information available from the gene. Even though the results suggest a positive correlation between the degree of a node and its morbidity while controlling the information factor, we believe this correlation has to be taken with caution for it can be affected by other factors not taken into account in this study.
CitationMassanet, R.; Caminal, P.; Perera, A. Graph theory-based measures as predictors of gene morbidity. "Annual International Conference of the IEEE Engineering in Medicine and Biology Society", 2010, vol. 2010, núm. 32, p. 803-806.