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Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes

Overview of attention for article published in BMC Bioinformatics, May 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

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4 tweeters
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1 Facebook page

Citations

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13 Dimensions

Readers on

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30 Mendeley
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1 CiteULike
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Title
Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes
Published in
BMC Bioinformatics, May 2016
DOI 10.1186/s12859-016-1087-5
Pubmed ID
Authors

Lina Zhang, Chengjin Zhang, Rui Gao, Runtao Yang, Qing Song

Abstract

Aptamer-protein interacting pairs play a variety of physiological functions and therapeutic potentials in organisms. Rapidly and effectively predicting aptamer-protein interacting pairs is significant to design aptamers binding to certain interested proteins, which will give insight into understanding mechanisms of aptamer-protein interacting pairs and developing aptamer-based therapies. In this study, an ensemble method is presented to predict aptamer-protein interacting pairs with hybrid features. The features for aptamers are extracted from Pseudo K-tuple Nucleotide Composition (PseKNC) while the features for proteins incorporate Discrete Cosine Transformation (DCT), disorder information, and bi-gram Position Specific Scoring Matrix (PSSM). We investigate predictive capabilities of various feature spaces. The proposed ensemble method obtains the best performance with Youden's Index of 0.380, using the hybrid feature space of PseKNC, DCT, bi-gram PSSM, and disorder information by 10-fold cross validation. The Relief-Incremental Feature Selection (IFS) method is adopted to obtain the optimal feature set. Based on the optimal feature set, the proposed method achieves a balanced performance with a sensitivity of 0.753 and a specificity of 0.725 on the training dataset, which indicates that this method can solve the imbalanced data problem effectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous study with a sensitivity of 0.738 and a Youden's Index of 0.451. These results suggest that the proposed method can be a potential candidate for aptamer-protein interacting pair prediction, which may contribute to finding novel aptamer-protein interacting pairs and understanding the relationship between aptamers and proteins.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 3%
Unknown 29 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 40%
Student > Ph. D. Student 4 13%
Other 3 10%
Student > Master 3 10%
Student > Postgraduate 2 7%
Other 3 10%
Unknown 3 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 30%
Agricultural and Biological Sciences 6 20%
Computer Science 5 17%
Chemistry 2 7%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 2 7%
Unknown 5 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 September 2016.
All research outputs
#6,707,267
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#2,500
of 4,588 outputs
Outputs of similar age
#110,896
of 270,293 outputs
Outputs of similar age from BMC Bioinformatics
#11
of 23 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,588 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
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 270,293 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 57% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.