A Method for Inferring Candidate Disease-Disease Associations

The analysis of Disease-Disease Associations (DDA) and Gene-Disease Associations (GDA) is a relevant task in bioinformatics. These are analysed to investigate the interactions between sets of diseases and genes as well as their similarity, e.g., to improve the phases of diagnosis, prognosis and treatment in medicine. Generally, the extraction of information of interest from large-scale data, usually heterogeneous and unstructured, is performed via time-consuming processes. Therefore, several computational approaches have been focused on their prediction through data integration and machine learning techniques. This paper presents a solution for Inferring DDA (IDDA) by integrating curated biomedical ontologies and medical dictionaries. It is able to extract a set of DDA using an in-house score based on the GDA. A preliminary step based on data enrichment retrieves the information about gene and disease, and it integrates these with a set of curated biological data ontologies and dictionaries. Specifically, IDDA extracts DDAs based on an in-house score, which uses GDAs for its evaluations. In a preliminary step, it performs data enrichment to retrieve concepts both for diseases and genes, by integrating several curated biomedical ontologies and medical dictionaries.