Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Motivation Drug repurposing speeds up the development of new treatments, being less costly, risky, and time consuming than de novo drug discovery. There are numerous biological elements that contribute to the development of diseases and, as a result, to the repurposing of drugs. Methods In this article, we analysed the potential role of protein sequences in drug repurposing scenarios. For this purpose, we embedded the protein sequences by performing four state of the art methods and validated their capacity to encapsulate essential biological information through visualization. Then, we compared the differences in sequence distance between protein-drug target pairs of drug repurposing and non - drug repurposing data. Thus, we were able to uncover patterns that define protein sequences in repurposing cases. Results We found statistically significant sequence distance differences between protein pairs in the repurposing data and the rest of protein pairs in non-repurposing data. In this manner, we verified the potential of using numerical representations of sequences to generate repurposing hypotheses in the future.
Motivation Drug repurposing speeds up the development of new treatments, being less costly, risky, and time consuming than de novo drug discovery. There are numerous biological elements that contribute to the development of diseases and, as a result, to the repurposing of drugs. Methods In this article, we analysed the potential role of protein sequences in drug repurposing scenarios. For this purpose, we embedded the protein sequences by performing four state of the art methods and validated their capacity to encapsulate essential biological information through visualization. Then, we compared the differences in sequence distance between protein-drug target pairs of drug repurposing and non - drug repurposing data. Thus, we were able to uncover patterns that define protein sequences in repurposing cases. Results We found statistically significant sequence distance differences between protein pairs in the repurposing data and the rest of protein pairs in non-repurposing data. In this manner, we verified the potential of using numerical representations of sequences to generate repurposing hypotheses in the future.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.