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Prediction of selective estrogen receptor beta agonist using open data and machine learning approach

Overview of attention for article published in Drug Design, Development and Therapy, July 2016
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

twitter
8 tweeters

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
25 Mendeley
Title
Prediction of selective estrogen receptor beta agonist using open data and machine learning approach
Published in
Drug Design, Development and Therapy, July 2016
DOI 10.2147/dddt.s110603
Pubmed ID
Authors

Shengqi Wang, Liang-jun Xie, Hui Wang, Bing Zhu, Ai-qin Niu

Abstract

Estrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis, and cardiovascular disease. ERs have two subtypes, ER-α and ER-β. Emerging data suggest that the development of subtype-selective ligands that specifically target ER-β could be a more optimal approach to elicit beneficial estrogen-like activities and reduce side effects. Herein, we focused on ER-β and developed its in silico quantitative structure-activity relationship models using machine learning (ML) methods. The chemical structures and ER-β bioactivity data were extracted from public chemogenomics databases. Four types of popular fingerprint generation methods including MACCS fingerprint, PubChem fingerprint, 2D atom pairs, and Chemistry Development Kit extended fingerprint were used as descriptors. Four ML methods including Naïve Bayesian classifier, k-nearest neighbor, random forest, and support vector machine were used to train the models. The range of classification accuracies was 77.10% to 88.34%, and the range of area under the ROC (receiver operating characteristic) curve values was 0.8151 to 0.9475, evaluated by the 5-fold cross-validation. Comparison analysis suggests that both the random forest and the support vector machine are superior for the classification of selective ER-β agonists. Chemistry Development Kit extended fingerprints and MACCS fingerprint performed better in structural representation between active and inactive agonists. These results demonstrate that combining the fingerprint and ML approaches leads to robust ER-β agonist prediction models, which are potentially applicable to the identification of selective ER-β agonists.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 20%
Student > Doctoral Student 4 16%
Student > Master 4 16%
Student > Bachelor 3 12%
Student > Postgraduate 2 8%
Other 3 12%
Unknown 4 16%
Readers by discipline Count As %
Medicine and Dentistry 6 24%
Computer Science 4 16%
Biochemistry, Genetics and Molecular Biology 2 8%
Arts and Humanities 1 4%
Mathematics 1 4%
Other 4 16%
Unknown 7 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 August 2016.
All research outputs
#3,900,100
of 14,977,987 outputs
Outputs from Drug Design, Development and Therapy
#200
of 1,531 outputs
Outputs of similar age
#74,057
of 264,901 outputs
Outputs of similar age from Drug Design, Development and Therapy
#9
of 65 outputs
Altmetric has tracked 14,977,987 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,531 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 86% 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 264,901 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 71% of its contemporaries.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.