↓ Skip to main content

Immunoinformatics and epitope prediction in the age of genomic medicine

Overview of attention for article published in Genome Medicine, November 2015
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
149 Dimensions

Readers on

mendeley
375 Mendeley
citeulike
5 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Immunoinformatics and epitope prediction in the age of genomic medicine
Published in
Genome Medicine, November 2015
DOI 10.1186/s13073-015-0245-0
Pubmed ID
Authors

Linus Backert, Oliver Kohlbacher

Abstract

Immunoinformatics involves the application of computational methods to immunological problems. Prediction of B- and T-cell epitopes has long been the focus of immunoinformatics, given the potential translational implications, and many tools have been developed. With the advent of next-generation sequencing (NGS) methods, an unprecedented wealth of information has become available that requires more-advanced immunoinformatics tools. Based on information from whole-genome sequencing, exome sequencing and RNA sequencing, it is possible to characterize with high accuracy an individual's human leukocyte antigen (HLA) allotype (i.e., the individual set of HLA alleles of the patient), as well as changes arising in the HLA ligandome (the collection of peptides presented by the HLA) owing to genomic variation. This has allowed new opportunities for translational applications of epitope prediction, such as epitope-based design of prophylactic and therapeutic vaccines, and personalized cancer immunotherapies. Here, we review a wide range of immunoinformatics tools, with a focus on B- and T-cell epitope prediction. We also highlight fundamental differences in the underlying algorithms and discuss the various metrics employed to assess prediction quality, comparing their strengths and weaknesses. Finally, we discuss the new challenges and opportunities presented by high-throughput data-sets for the field of epitope prediction.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 375 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 <1%
Germany 1 <1%
Italy 1 <1%
Ireland 1 <1%
India 1 <1%
United States 1 <1%
Unknown 367 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 69 18%
Researcher 61 16%
Student > Ph. D. Student 61 16%
Student > Bachelor 59 16%
Student > Doctoral Student 20 5%
Other 54 14%
Unknown 51 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 91 24%
Agricultural and Biological Sciences 82 22%
Medicine and Dentistry 32 9%
Immunology and Microbiology 31 8%
Computer Science 25 7%
Other 46 12%
Unknown 68 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 November 2015.
All research outputs
#9,541,688
of 16,534,657 outputs
Outputs from Genome Medicine
#958
of 1,107 outputs
Outputs of similar age
#163,497
of 370,275 outputs
Outputs of similar age from Genome Medicine
#98
of 108 outputs
Altmetric has tracked 16,534,657 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,107 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.6. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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 370,275 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 54% of its contemporaries.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.