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Machine learning techniques in disease forecasting: a case study on rice blast prediction

Overview of attention for article published in BMC Bioinformatics, November 2006
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
85 Dimensions

Readers on

mendeley
203 Mendeley
citeulike
1 CiteULike
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Title
Machine learning techniques in disease forecasting: a case study on rice blast prediction
Published in
BMC Bioinformatics, November 2006
DOI 10.1186/1471-2105-7-485
Pubmed ID
Authors

Rakesh Kaundal, Amar S Kapoor, Gajendra PS Raghava

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 203 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 3 1%
Sweden 1 <1%
Kenya 1 <1%
Israel 1 <1%
Malaysia 1 <1%
United States 1 <1%
Unknown 195 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 49 24%
Student > Master 29 14%
Researcher 29 14%
Student > Bachelor 19 9%
Other 9 4%
Other 36 18%
Unknown 32 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 45 22%
Computer Science 44 22%
Engineering 27 13%
Environmental Science 11 5%
Biochemistry, Genetics and Molecular Biology 8 4%
Other 27 13%
Unknown 41 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 20 March 2018.
All research outputs
#7,952,000
of 12,680,099 outputs
Outputs from BMC Bioinformatics
#3,229
of 4,716 outputs
Outputs of similar age
#163,474
of 274,286 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 24 outputs
Altmetric has tracked 12,680,099 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,716 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 22nd percentile – i.e., 22% 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 274,286 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 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 58% of its contemporaries.