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Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification

Overview of attention for article published in BioData Mining, February 2016
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2 X users
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2 Wikipedia pages

Citations

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26 Dimensions

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50 Mendeley
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Title
Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification
Published in
BioData Mining, February 2016
DOI 10.1186/s13040-016-0089-1
Pubmed ID
Authors

Mona Riemenschneider, Robin Senge, Ursula Neumann, Eyke Hüllermeier, Dominik Heider

Abstract

Antiretroviral therapy is essential for human immunodeficiency virus (HIV) infected patients to inhibit viral replication and therewith to slow progression of disease and prolong a patient's life. However, the high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure and thereby to the evolution of resistant variants. In turn, these variants will lead to the failure of antiretroviral treatment. Moreover, these mutations cannot only lead to resistance against single drugs, but also to cross-resistance, i.e., resistance against drugs that have not yet been applied. 662 protease sequences and 715 reverse transcriptase sequences with complete resistance profiles were analyzed using machine learning techniques, namely binary relevance classifiers, classifier chains, and ensembles of classifier chains. In our study, we applied multi-label classification models incorporating cross-resistance information to predict drug resistance for two of the major drug classes used in antiretroviral therapy for HIV-1, namely protease inhibitors (PIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs). By means of multi-label learning, namely classifier chains (CCs) and ensembles of classifier chains (ECCs), we were able to improve overall prediction accuracy for all drugs compared to hitherto applied binary classification models. The development of fast and precise models to predict drug resistance in HIV-1 is highly important to enable a highly effective personalized therapy. Cross-resistance information can be exploited to improve prediction accuracy of computational drug resistance models.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
South Africa 1 2%
Unknown 49 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 18%
Student > Ph. D. Student 7 14%
Student > Doctoral Student 4 8%
Student > Bachelor 4 8%
Student > Postgraduate 4 8%
Other 8 16%
Unknown 14 28%
Readers by discipline Count As %
Computer Science 10 20%
Medicine and Dentistry 6 12%
Biochemistry, Genetics and Molecular Biology 6 12%
Agricultural and Biological Sciences 2 4%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Other 7 14%
Unknown 17 34%
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 05 October 2023.
All research outputs
#7,064,959
of 24,569,575 outputs
Outputs from BioData Mining
#145
of 316 outputs
Outputs of similar age
#92,531
of 302,861 outputs
Outputs of similar age from BioData Mining
#9
of 13 outputs
Altmetric has tracked 24,569,575 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 316 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has gotten more attention than average, scoring higher than 53% 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 302,861 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 68% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.