For example, we do not consider functional data sources, such as gene expression or protein level measurements, or descriptive higher-level information, such as gene ontology.
In fact, this should facilitate a gene centric analysis and automatic integration with functional data sources.
Although there have been prior integration efforts of structural data and functional data sources, the PROFESS system has a unique approach.
Check box Select functional data source Ensembl putative functional variants, ENCODE regulatory feature peaks, Expression quantitative trait loci (eQTLs) Ensembl putative functional variants, ENCODE regulatory feature peaks, expression quantitative trait loci (eQTLs) Functional annotation data source for Ensembl.
Finally, we show that our results are robust to a battery of sensitivity tests involving functional forms, data sources for the largest countries, methods of interpolating and extrapolating missing data, and dealing with survey misreporting.
However, those approaches are limited in that functional annotation data sources have not covered whole human genome yet.
These network-based methods are mostly based on biological networks, which are constructed based on various kinds of biomedical data, and therefore they are not limited by the coverage of functional annotation data sources.
In which, functional annotation-based methods have prioritized candidate genes by measuring the degree of similarity of each candidate genes to a set of known disease genes based on profiles which were built from many functional annotation data sources [6, 7, 8].
These approaches rely on efficient exploitation of functional genomics data sources (Giallourakis et al., 2005) and take advantage of numerous computer-based systems that have been developed in the last 5 years.
In this paper, we developed a method called BRIDGE to integrate the disease phenotypic similarity with such functional genomic data sources as protein-protein interactions, gene sequence similarity, gene expression profiles, gene pathway annotations, and gene ontology annotations to prioritize candidate genes for the discovery of disease genes.
Based on the integrated protein-protein and functional interaction data source, the DNA methylation values of the any gene pair interacted with each other were calculated by the similarity measure of Pearson correlation.
