Abstract
BackgroundPrioritization methods have become an useful tool for mining large amounts of data to suggest promising hypotheses in early research stages. Particularly, network-based prioritization tools use a network representation for the interactions between different biological entities to identify novel indirect relationships. However, current network-based prioritization tools are strongly tailored to specific domains of interest (e.g. gene-disease prioritization) and they do not allow to consider networks with more than two types of entities (e.g. genes and diseases). Therefore, the direct application of these methods to accomplish new prioritization tasks is limited.ResultsThis work presents ProphNet, a generic network-based prioritization tool that allows to integrate an arbitrary number of interrelated biological entities to accomplish any prioritization task. We tested the performance of ProphNet in comparison with leading network-based prioritization methods, namely rcNet and DomainRBF, for gene-disease and domain-disease prioritization, respectively. The results obtained by ProphNet show a significant improvement in terms of sensitivity and specificity for both tasks. We also applied ProphNet to disease-gene prioritization on Alzheimer, Diabetes Mellitus Type 2 and Breast Cancer to validate the results and identify putative candidate genes involved in these diseases.ConclusionsProphNet works on top of any heterogeneous network by integrating information of different types of biological entities to rank entities of a specific type according to their degree of relationship with a query set of entities of another type. Our method works by propagating information across data networks and measuring the correlation between the propagated values for a query and a target sets of entities. ProphNet is available at: http://genome2.ugr.es/prophnet. A Matlab implementation of the algorithm is also available at the website.
Highlights
Prioritization methods have become an useful tool for mining large amounts of data to suggest promising hypotheses in early research stages
ProphNet is applied to several case studies to identify putative candidate genes involved in these diseases
As two specific case studies, we have applied ProphNet to prioritize candidate genes and protein domains associated to diseases
Summary
Prioritization methods have become an useful tool for mining large amounts of data to suggest promising hypotheses in early research stages. The advancements in high-throughput technologies such as DNA sequencing, linkage analysis, association studies and expression arrays have fostered the research towards an effective personalized medicine To this end, the integration of pieces of evidence of different nature derived from diverse data sources is required, together with algorithms able to mine these data and identify novel biological facts of relevance. Gene-disease prioritization, in which genes are ranked according to their relevance to a disease of interest (or vice versa), is the most popular prioritization task, and many methods have been proposed in the recent literature to accomplish this task [6] Most of these methods focus on the analysis of phenotype and PPI networks for gene-disease prioritization. Network-based genedisease prioritization methods have been proven to provide better results than previous approaches [11,12,13,14,15]
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