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Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

Overview of attention for article published in BMC Bioinformatics, October 2009
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3 X users

Citations

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Title
Structuring and extracting knowledge for the support of hypothesis generation in molecular biology
Published in
BMC Bioinformatics, October 2009
DOI 10.1186/1471-2105-10-s10-s9
Pubmed ID
Authors

Marco Roos, M Scott Marshall, Andrew P Gibson, Martijn Schuemie, Edgar Meij, Sophia Katrenko, Willem Robert van Hage, Konstantinos Krommydas, Pieter W Adriaans

Abstract

Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 5 7%
Brazil 3 4%
United States 3 4%
Mexico 3 4%
United Kingdom 2 3%
Sweden 1 1%
Finland 1 1%
Unknown 53 75%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 30%
Student > Master 9 13%
Student > Ph. D. Student 8 11%
Professor > Associate Professor 6 8%
Student > Bachelor 5 7%
Other 13 18%
Unknown 9 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 32%
Computer Science 22 31%
Medicine and Dentistry 5 7%
Engineering 4 6%
Linguistics 1 1%
Other 4 6%
Unknown 12 17%
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 21 April 2017.
All research outputs
#12,906,644
of 22,771,140 outputs
Outputs from BMC Bioinformatics
#3,782
of 7,273 outputs
Outputs of similar age
#74,458
of 93,360 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 58 outputs
Altmetric has tracked 22,771,140 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 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 45th percentile – i.e., 45% 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 93,360 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.