INTEGRO: an algorithm for data-integration and disease-gene association

Biological information are continuously analyzed and collected by health-informatics tools for knowledge extraction. For instance, Next Generation Sequencing (NGS) and Micro Array technologies produce a large amount of genomic and biological data that are analyzed in clinical research trials to evaluate novel treatments. In bioinformatics many solutions has been designed using data -mining and -integration techniques to collect heterogeneous data useful to correlate human diseases and genomic data.In this paper, a python algorithm for data-integration and disease-gene association is presented. It integrates a set of curated sources (DO, OMIM, SNOMED-CT, NCBI-MedGen, and DisGeNET) establishing new relations in order to acquire as much information as possible for a specific disease in input, as well as the disease-gene associations.