↓ Skip to main content

Improving protein fold recognition by random forest

Overview of attention for article published in BMC Bioinformatics, October 2014
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
1 X user

Citations

dimensions_citation
54 Dimensions

Readers on

mendeley
37 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Improving protein fold recognition by random forest
Published in
BMC Bioinformatics, October 2014
DOI 10.1186/1471-2105-15-s11-s14
Pubmed ID
Authors

Taeho Jo, Jianlin Cheng

Abstract

Recognizing the correct structural fold among known template protein structures for a target protein (i.e. fold recognition) is essential for template-based protein structure modeling. Since the fold recognition problem can be defined as a binary classification problem of predicting whether or not the unknown fold of a target protein is similar to an already known template protein structure in a library, machine learning methods have been effectively applied to tackle this problem. In our work, we developed RF-Fold that uses random forest - one of the most powerful and scalable machine learning classification methods - to recognize protein folds.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 32%
Researcher 6 16%
Student > Master 3 8%
Student > Bachelor 2 5%
Other 1 3%
Other 2 5%
Unknown 11 30%
Readers by discipline Count As %
Computer Science 6 16%
Agricultural and Biological Sciences 6 16%
Biochemistry, Genetics and Molecular Biology 4 11%
Environmental Science 3 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 6 16%
Unknown 11 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 25 October 2014.
All research outputs
#15,309,583
of 22,769,322 outputs
Outputs from BMC Bioinformatics
#5,373
of 7,273 outputs
Outputs of similar age
#151,153
of 259,770 outputs
Outputs of similar age from BMC Bioinformatics
#94
of 132 outputs
Altmetric has tracked 22,769,322 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 259,770 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.