Some of the main challenges

About DISGENET plus


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

The most comprehensive platform on disease genomics
Coverage of all therapeutic areas
Up-to-date catalog of genes and genomic variants
Powerful data analytics capabilities
A text mining engine using deep learning approaches, tailored to the peculiarities of the biomedical literature

  • Most disease genomics comprehensive platform

  • Latest technology

Our expertise is accredited in our more than 10 years of experience in developing DISGENET,

a resource widely used in the biomedical community with over 50,000 users per year, and more than 2,500 bibliographic citations.

In DISGENET plus we have incorporated:

  • Deep learning models to detect mentions of diseases and genes
  • New module to manage acronyms & abbreviations
  • Deep learning models to detect and characterize negations
  • New module to detect mention of animal models

Increased performance score (F) to 92%

DISGENET plus engine improves the detection and normalization of mentions of genes and diseases by relying on deep learning models complemented by an extensive set of custom heuristics. New text analysis modules have been integrated into DISGENET plus so as to consistently detect and characterize distinctive linguistic traits of biomedical texts like the use of acronyms, abbreviations, and negations.

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.