Network-based enrichment analysis of gene expression through protein-protein interaction data.

High-throughput analysis of gene expression data is subject to technological and statistical issues that confuse the underlying expression-condition associations. In this contribution a network-based candidate gene prioritization strategy was applied to the enrichment of a publicly available gene expression dataset, focused on the study of the mechanosensitivity of genes exposed to altered pulmonary matrix stiffness. Results suggested that some genes which had not been taken into account in the original study could have an important role in the processes causing, or affected by, pulmonary fibrosis.

Analysis of incomplete gene expression dataset through protein-protein interaction information.

This paper shows a graph based method to analyze proteomic expression data. The method allows the prediction of the expression of genes not measured by the gene expression technology based on the local connectivity properties of the measured differentially expressed gene set. The prediction of the expression jointly with the stability of this prediction as a function of the variation of the initial expressed set is computed. The method is able to correctly predict one third of the proteins with independence of variations on the selection of the initial set. The algorithm is validated through a Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometer (MALDI-TOF) protein expression experiment aiming the study of the protein expression patterns and post-translational modifications in human endothelial vascular cells exposed to atherosclerotic levels of Low Density Lipoproteins (LDL).

Graph theory-based measures as predictors of gene morbidity.

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.

An information theory-based tool for characterizing the interaction environment of a protein.

In recent years large amounts of information have been accumulated in proteomic, genetic and metabolic databases. Much effort has been dedicated to developing methods that successfully exploit, organize and structure this information. However, there is no application, that we know of, that semantically characterizes the interaction environment in which a protein exists. A high-throughput software package has been developed to retrieve information from publicly available databases, such as the Gene Ontology Annotation (GOA) database and the Human Proteome Resource Database (HPRD) and structure their information. This information is presented to the user as groups of semantically described dense interaction subnetworks that interact with a target protein.