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Attention Score in Context
| Title |
DeepFilter: A Deep Learning Based Variant Filter for VarDict
|
|---|---|
| Published in |
Tsinghua Science & Technology, August 2023
|
| DOI | 10.26599/tst.2022.9010032 |
| Authors |
Hao Zhang, Zekun Yin, Yanjie Wei, Bertil Schmidt, Weiguo Liu |
Attention Score in Context
This research output has an Altmetric Attention Score of 28. 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 01 February 2023.
All research outputs
#1,403,308
of 25,392,582 outputs
Outputs from Tsinghua Science & Technology
#2
of 220 outputs
Outputs of similar age
#24,768
of 358,370 outputs
Outputs of similar age from Tsinghua Science & Technology
#1
of 1 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 220 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done particularly well, scoring higher than 99% 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 358,370 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them