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A multiple kernel learning algorithm for drug-target interaction prediction

Overview of attention for article published in BMC Bioinformatics, January 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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9 X users
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1 Facebook page
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2 Google+ users

Citations

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163 Dimensions

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190 Mendeley
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Title
A multiple kernel learning algorithm for drug-target interaction prediction
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-016-0890-3
Pubmed ID
Authors

André C. A. Nascimento, Ricardo B. C. Prudêncio, Ivan G. Costa

Abstract

Drug-target networks are receiving a lot of attention in late years, given its relevance for pharmaceutical innovation and drug lead discovery. Different in silico approaches have been proposed for the identification of new drug-target interactions, many of which are based on kernel methods. Despite technical advances in the latest years, these methods are not able to cope with large drug-target interaction spaces and to integrate multiple sources of biological information. We propose KronRLS-MKL, which models the drug-target interaction problem as a link prediction task on bipartite networks. This method allows the integration of multiple heterogeneous information sources for the identification of new interactions, and can also work with networks of arbitrary size. Moreover, it automatically selects the more relevant kernels by returning weights indicating their importance in the drug-target prediction at hand. Empirical analysis on four data sets using twenty distinct kernels indicates that our method has higher or comparable predictive performance than 18 competing methods in all prediction tasks. Moreover, the predicted weights reflect the predictive quality of each kernel on exhaustive pairwise experiments, which indicates the success of the method to automatically reveal relevant biological sources. Our analysis show that the proposed data integration strategy is able to improve the quality of the predicted interactions, and can speed up the identification of new drug-target interactions as well as identify relevant information for the task. The source code and data sets are available at www.cin.ufpe.br/~acan/kronrlsmkl/ .

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 190 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Bulgaria 1 <1%
New Caledonia 1 <1%
Brazil 1 <1%
Spain 1 <1%
Japan 1 <1%
Unknown 185 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 24%
Student > Master 31 16%
Researcher 22 12%
Student > Bachelor 10 5%
Professor > Associate Professor 9 5%
Other 27 14%
Unknown 46 24%
Readers by discipline Count As %
Computer Science 55 29%
Agricultural and Biological Sciences 16 8%
Biochemistry, Genetics and Molecular Biology 15 8%
Pharmacology, Toxicology and Pharmaceutical Science 11 6%
Medicine and Dentistry 8 4%
Other 28 15%
Unknown 57 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 02 April 2016.
All research outputs
#4,653,686
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#1,736
of 7,387 outputs
Outputs of similar age
#81,494
of 397,704 outputs
Outputs of similar age from BMC Bioinformatics
#35
of 141 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 76% 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 397,704 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 79% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.