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The Interaction Network Ontology-supported modeling and mining of complex interactions represented with multiple keywords in biomedical literature

Overview of attention for article published in BioData Mining, December 2016
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Title
The Interaction Network Ontology-supported modeling and mining of complex interactions represented with multiple keywords in biomedical literature
Published in
BioData Mining, December 2016
DOI 10.1186/s13040-016-0118-0
Pubmed ID
Authors

Arzucan Özgür, Junguk Hur, Yongqun He

Abstract

The Interaction Network Ontology (INO) logically represents biological interactions, pathways, and networks. INO has been demonstrated to be valuable in providing a set of structured ontological terms and associated keywords to support literature mining of gene-gene interactions from biomedical literature. However, previous work using INO focused on single keyword matching, while many interactions are represented with two or more interaction keywords used in combination. This paper reports our extension of INO to include combinatory patterns of two or more literature mining keywords co-existing in one sentence to represent specific INO interaction classes. Such keyword combinations and related INO interaction type information could be automatically obtained via SPARQL queries, formatted in Excel format, and used in an INO-supported SciMiner, an in-house literature mining program. We studied the gene interaction sentences from the commonly used benchmark Learning Logic in Language (LLL) dataset and one internally generated vaccine-related dataset to identify and analyze interaction types containing multiple keywords. Patterns obtained from the dependency parse trees of the sentences were used to identify the interaction keywords that are related to each other and collectively represent an interaction type. The INO ontology currently has 575 terms including 202 terms under the interaction branch. The relations between the INO interaction types and associated keywords are represented using the INO annotation relations: 'has literature mining keywords' and 'has keyword dependency pattern'. The keyword dependency patterns were generated via running the Stanford Parser to obtain dependency relation types. Out of the 107 interactions in the LLL dataset represented with two-keyword interaction types, 86 were identified by using the direct dependency relations. The LLL dataset contained 34 gene regulation interaction types, each of which associated with multiple keywords. A hierarchical display of these 34 interaction types and their ancestor terms in INO resulted in the identification of specific gene-gene interaction patterns from the LLL dataset. The phenomenon of having multi-keyword interaction types was also frequently observed in the vaccine dataset. By modeling and representing multiple textual keywords for interaction types, the extended INO enabled the identification of complex biological gene-gene interactions represented with multiple keywords.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 26 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 19%
Student > Master 3 11%
Student > Bachelor 3 11%
Student > Doctoral Student 2 7%
Other 2 7%
Other 7 26%
Unknown 5 19%
Readers by discipline Count As %
Computer Science 9 33%
Engineering 3 11%
Agricultural and Biological Sciences 3 11%
Biochemistry, Genetics and Molecular Biology 2 7%
Medicine and Dentistry 1 4%
Other 3 11%
Unknown 6 22%

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 27 December 2016.
All research outputs
#10,689,811
of 17,800,904 outputs
Outputs from BioData Mining
#193
of 277 outputs
Outputs of similar age
#198,263
of 394,522 outputs
Outputs of similar age from BioData Mining
#32
of 40 outputs
Altmetric has tracked 17,800,904 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 277 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 27th percentile – i.e., 27% 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 394,522 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.