↓ Skip to main content

DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning

Overview of attention for article published in BMC Bioinformatics, December 2018
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

twitter
6 X users

Citations

dimensions_citation
108 Dimensions

Readers on

mendeley
75 Mendeley
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
DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning
Published in
BMC Bioinformatics, December 2018
DOI 10.1186/s12859-018-2516-4
Pubmed ID
Authors

Yiqian Zhang, Michiaki Hamada

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 75 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 19%
Student > Master 11 15%
Researcher 9 12%
Student > Bachelor 8 11%
Student > Doctoral Student 4 5%
Other 14 19%
Unknown 15 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 22 29%
Agricultural and Biological Sciences 9 12%
Computer Science 8 11%
Medicine and Dentistry 7 9%
Nursing and Health Professions 2 3%
Other 6 8%
Unknown 21 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 05 January 2019.
All research outputs
#13,174,456
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#3,692
of 7,418 outputs
Outputs of similar age
#199,902
of 440,077 outputs
Outputs of similar age from BMC Bioinformatics
#91
of 216 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 48th percentile – i.e., 48% 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 440,077 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 216 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 55% of its contemporaries.