The DisGeNET knowledge platform for disease genomics: 2019 update

Clicks: 1
ID: 290449
2019
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #893 of 1,216 articles by views in Nucleic Acids Research

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,216 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.
Reference Key
openalex_W2983166786 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Janet Piñero, Juan Manuel Ramírez‐Anguita, Josep Saüch-Pitarch, Francesco Ronzano, Emilio Centeno, Ferrán Sanz, Laura I. Furlong
Journal Nucleic Acids Research
Year 2019
DOI
10.1093/nar/gkz1021
URL
https://academic.oup.com/nar/article-pdf/48/D1/D845/31697865/gkz1021.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.