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Predicting drug target interactions using meta-path-based semantic network analysis

Overview of attention for article published in BMC Bioinformatics, April 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)

Mentioned by

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16 tweeters

Citations

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50 Dimensions

Readers on

mendeley
73 Mendeley
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2 CiteULike
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Title
Predicting drug target interactions using meta-path-based semantic network analysis
Published in
BMC Bioinformatics, April 2016
DOI 10.1186/s12859-016-1005-x
Pubmed ID
Authors

Gang Fu, Ying Ding, Abhik Seal, Bin Chen, Yizhou Sun, Evan Bolton

Abstract

In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction. Supervised machine learning models were constructed based on meta-path topological features of an enriched semantic network, which was derived from Chem2Bio2RDF, and was expanded by adding compound and protein similarity neighboring links obtained from the PubChem databases. The additional semantic links significantly improved the predictive performance of the supervised learning models. The binary classification model built upon the enriched feature space using the Random Forest algorithm significantly outperformed an existing semantic link prediction algorithm, Semantic Link Association Prediction (SLAP), to predict unknown links between compounds and protein targets in an evolving network. In addition to link prediction, Random Forest also has an intrinsic feature ranking algorithm, which can be used to select the important topological features that contribute to link prediction. The proposed framework has been demonstrated as a powerful alternative to SLAP in order to predict DTIs using the semantic network that integrates chemical, pharmacological, genomic, biological, functional, and biomedical information into a unified framework. It offers the flexibility to enrich the feature space by using different normalization processes on the topological features, and it can perform model construction and feature selection at the same time.

Twitter Demographics

The data shown below were collected from the profiles of 16 tweeters 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
Unknown 72 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 32%
Researcher 12 16%
Student > Master 9 12%
Student > Bachelor 5 7%
Professor 5 7%
Other 11 15%
Unknown 8 11%
Readers by discipline Count As %
Computer Science 22 30%
Biochemistry, Genetics and Molecular Biology 8 11%
Chemistry 6 8%
Agricultural and Biological Sciences 5 7%
Engineering 4 5%
Other 17 23%
Unknown 11 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 11 January 2019.
All research outputs
#2,087,031
of 14,123,515 outputs
Outputs from BMC Bioinformatics
#883
of 5,337 outputs
Outputs of similar age
#49,713
of 263,995 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 14,123,515 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,337 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 83% 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 263,995 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them