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eRAM: encyclopedia of rare disease annotations for precision medicine

Overview of attention for article published in Nucleic Acids Research, November 2017
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

twitter
7 tweeters

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
46 Mendeley
Title
eRAM: encyclopedia of rare disease annotations for precision medicine
Published in
Nucleic Acids Research, November 2017
DOI 10.1093/nar/gkx1062
Pubmed ID
Authors

Jinmeng Jia, Zhongxin An, Yue Ming, Yongli Guo, Wei Li, Yunxiang Liang, Dongming Guo, Xin Li, Jun Tai, Geng Chen, Yaqiong Jin, Zhimei Liu, Xin Ni, Tieliu Shi

Abstract

Rare diseases affect over a hundred million people worldwide, most of these patients are not accurately diagnosed and effectively treated. The limited knowledge of rare diseases forms the biggest obstacle for improving their treatment. Detailed clinical phenotyping is considered as a keystone of deciphering genes and realizing the precision medicine for rare diseases. Here, we preset a standardized system for various types of rare diseases, called encyclopedia of Rare disease Annotations for Precision Medicine (eRAM). eRAM was built by text-mining nearly 10 million scientific publications and electronic medical records, and integrating various data in existing recognized databases (such as Unified Medical Language System (UMLS), Human Phenotype Ontology, Orphanet, OMIM, GWAS). eRAM systematically incorporates currently available data on clinical manifestations and molecular mechanisms of rare diseases and uncovers many novel associations among diseases. eRAM provides enriched annotations for 15 942 rare diseases, yielding 6147 human disease related phenotype terms, 31 661 mammalians phenotype terms, 10,202 symptoms from UMLS, 18 815 genes and 92 580 genotypes. eRAM can not only provide information about rare disease mechanism but also facilitate clinicians to make accurate diagnostic and therapeutic decisions towards rare diseases. eRAM can be freely accessed at http://www.unimd.org/eram/.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 22%
Student > Ph. D. Student 5 11%
Professor 4 9%
Student > Bachelor 4 9%
Student > Master 3 7%
Other 9 20%
Unknown 11 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 28%
Medicine and Dentistry 6 13%
Computer Science 5 11%
Agricultural and Biological Sciences 3 7%
Nursing and Health Professions 2 4%
Other 5 11%
Unknown 12 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 November 2017.
All research outputs
#4,381,793
of 15,466,176 outputs
Outputs from Nucleic Acids Research
#8,516
of 22,608 outputs
Outputs of similar age
#105,169
of 322,261 outputs
Outputs of similar age from Nucleic Acids Research
#121
of 317 outputs
Altmetric has tracked 15,466,176 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 22,608 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 322,261 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 317 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.