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Learning from biomedical linked data to suggest valid pharmacogenes

Overview of attention for article published in Journal of Biomedical Semantics, April 2017
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  • Above-average Attention Score compared to outputs of the same age (63rd percentile)

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Title
Learning from biomedical linked data to suggest valid pharmacogenes
Published in
Journal of Biomedical Semantics, April 2017
DOI 10.1186/s13326-017-0125-1
Pubmed ID
Authors

Kevin Dalleau, Yassine Marzougui, Sébastien Da Silva, Patrice Ringot, Ndeye Coumba Ndiaye, Adrien Coulet

Abstract

A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, only molecular networks or the biomedical literature were used, whereas many other resources are available. We propose here to consume a diverse and larger set of resources using linked data related either to genes, drugs or diseases. One of the advantages of linked data is that they are built on a standard framework that facilitates the joint use of various sources, and thus facilitates considering features of various origins. We propose a selection and linkage of data sources relevant to pharmacogenomics, including for example DisGeNET and Clinvar. We use machine learning to identify and prioritize pharmacogenes that are the most probably valid, considering the selected linked data. This identification relies on the classification of gene-drug pairs as either pharmacogenomically associated or not and was experimented with two machine learning methods -random forest and graph kernel-, which results are compared in this article. We assembled a set of linked data relative to pharmacogenomics, of 2,610,793 triples, coming from six distinct resources. Learning from these data, random forest enables identifying valid pharmacogenes with a F-measure of 0.73, on a 10 folds cross-validation, whereas graph kernel achieves a F-measure of 0.81. A list of top candidates proposed by both approaches is provided and their obtention is discussed.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 25%
Researcher 5 13%
Other 4 10%
Student > Master 4 10%
Student > Bachelor 3 8%
Other 6 15%
Unknown 8 20%
Readers by discipline Count As %
Computer Science 12 30%
Medicine and Dentistry 4 10%
Agricultural and Biological Sciences 4 10%
Biochemistry, Genetics and Molecular Biology 2 5%
Nursing and Health Professions 2 5%
Other 6 15%
Unknown 10 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 04 June 2017.
All research outputs
#7,015,001
of 22,965,074 outputs
Outputs from Journal of Biomedical Semantics
#131
of 364 outputs
Outputs of similar age
#111,185
of 310,204 outputs
Outputs of similar age from Journal of Biomedical Semantics
#4
of 6 outputs
Altmetric has tracked 22,965,074 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 61% 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 310,204 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 63% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.