Journal
NUCLEIC ACIDS RESEARCH
Volume 49, Issue W1, Pages W153-W161Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkab449
Keywords
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Categories
Funding
- UK Research and Innovation
- Medical Research Council
- South London and Maudsley NHS Foundation Trust
- MND Scotland
- Motor Neurone Disease Association
- National Institute for Health Research
- China Scholarship Council
- Spastic Paraplegia Foundation
- UKRI Innovation Fellowship [UK MR/S00310X/1]
- MotorNeurone Disease Association
- King's-China Scholarship Council PhD Scholarship programme
- United Kingdom, Medical Research Council [MR/L501529/1, MR/R024804/1]
- Economic and Social Research Council [ES/L008238/1]
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London
- European Community's Horizon 2020 Programme [633413]
- Maudsley Charity [980]
- Guy's and St Thomas' Charity [TR130505]
- H2020 Societal Challenges Programme [633413] Funding Source: H2020 Societal Challenges Programme
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The rapid progress in understanding genetics underlying biological processes has led to the development of DGLinker, a webserver that uses machine learning models to predict novel genetic factors associated with human diseases. DGLinker allows users to explore biomedical information, generate knowledge graphs, and predict new disease-associated genes, making it a valuable tool for researchers in the field.
As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration.
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