Graph theory-based measures as predictors of gene morbidity
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/13147
Tipus de documentArticle
Data publicació2010
EditorIEEE Press. Institute of Electrical and Electronics Engineers
Condicions d'accésAccés obert
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
Abstract
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.
CitacióMassanet, 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.
ISSN1557-170X
Versió de l'editorhttp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05626521
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
05626521.pdf | 276,6Kb | Visualitza/Obre |