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DISNET: Drug repositioning and disease understanding through complex networks creation and analysis
Abstract
The DISNET project (https://disnet.ctb.upm.es) was conceived in the context of drug repurposing, aiming to build a large-scale disease network and integrating heterogeneous biomedical knowledge. It is being carried out in the Medical Data Analytics Laboratory (MEDAL) located at the Center for Biomedical Technology (CTB) of the Universidad Politécnica de Madrid (UPM). The principal objective of the DISNET project is to build a platform that enables researchers the creation of complex multilayer networks following the concepts of Human Disease Networks (HDNs), with the final purpose of generating new drug repurposing hypotheses. During recent years, DISNET has put together in an accessible knowledge base heterogeneous information that includes biomedical data obtained and integrated from both structured and unstructured sources. These data are organized in 3 topological levels: i) the phenotypic layer (with information regarding diseases and their associated symptoms); ii) the biologic layer (which stores molecular-shifted data related to diseases including genes, proteins, metabolic pathways, genetic variants, non-coding RNAs and so on); and iii) the pharmacologic layer (containing information of the drugs, their interactions and their connections to diseases). The main results derived from the execution of DISNET include a system able to automatically extract disease-symptom associations from different data sources by using Natural Language Processing (NLP). In the drug repurposing area, the DISNET project has suggested a data-driven methodology to evaluate new potentially repurposable drugs centred on disease-gene and disease-phenotype associations, and thus detecting significant differences between repurposing and non-repurposing data. In addition, a straightforward drug repurposing approach has been described for the particular case of COVID-19and other efforts have been made in the scope of rare diseases. Currently, new avenues are being explored to predict drug-disease links in the DISNET network by means of Graph Neural Networks (GNNs). Furthermore, the problems of classifying diseases in better nosological modelsand of mapping disease vocabularieshave also been tackled.
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References
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