Main challenges in disease genomics



Most human diseases are influenced by our genes. Identifying the genes that cause diseases is key to pinpoint novel strategies for prevention and treatment.

Human genetics offers the potential to identify new therapeutic targets and prioritize decision-making throughout the process of drug discovery and development.
But finding and putting together all this information is a cumbersome and expensive process. With DISGENET plus we provide a solution to these challenges.

  • Brings together the power of AI and genetics

  • Makes easy the access to disease genomics data

Based on the community-recognized DisGeNET platform, a resource widely used in the biomedical community with over 70,000 users per year, cited by more than 2,500 publications and one the ELIXIR Recommended Interoperability Resources.

DISGENET plus key features:

  • State-of-the-art text mining technology (deep learning and language models)
  • Increased coverage of animal models
  • Metrics and scores to prioritize the information
  • Most recent findings from the literature readily available

Text mining performance of 92% (F-score)

Check our use cases

Selected Publications

Our foundation comes from the first step of innovation, research.

J. Piñero, J. M. Ramírez-Anguita, J. Saüch-Pitarch, F. Ronzano, E. Centeno, F. Sanz, and L. I. Furlong. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Research, 48(D1):D845–D855, 2020. doi:10.1093/nar/gkz1021.

J. Piñero, A. Bravo, N. Queralt-Rosinach, A. Gutiérrez-Sacristán, J. Deu-Pons, E. Cen- teno, J. García-García, F. Sanz, L. I. Furlong, À. Bravo, N. Queralt-Rosinach, A. Gutiérrez-Sacristán, J. Deu-Pons, E. Centeno, J. García-García, F. Sanz, and L. I. Furlong. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Research, 45(D1):D833–D839, 2017. doi:10.1093/nar/gkw943.

N. Queralt-Rosinach, J. Piñero, À. Bravo, F. Sanz, and L. I. Furlong. DisGeNET-RDF: harnessing the innovative power of the Semantic Web to explore the genetic basis of diseases. Bioinformatics, 32(14):2236–2238, 2016. doi:10.1093/bioinformatics/btw214.

N. Queralt-Rosinach, T. Kuhn, C. Chichester, M. Dumontier, F. Sanz, and L. I. Fur- long. Publishing DisGeNET as nanopublications. Semantic Web, 7(5):519–528, 2016. doi:10.3233/SW-150189.

J. Piñero, N. Queralt-Rosinach, À. Bravo, J. Deu-Pons, A. Bauer-Mehren, M. Baron, F. Sanz, and L. I. Furlong. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database, 2015, 2015. doi:10.1093/database/bav028.

A. Bauer-Mehren, M. Bundschus, M. Rautschka, M. A. Mayer, F. Sanz, and L. I. Furlong. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases. PloS One, 6(6):e20284, 2011. doi:10.1371/journal.pone.0020284.

A. Bauer-Mehren, M. Rautschka, F. Sanz, and L. I. Furlong. DisGeNET: a Cytoscape plugin to visualize, integrate, search and analyze gene–disease networks. Bioinformatics, 26(22):2924–2926, 2010. doi:10.1093/bioinformatics/btq538.