↓ Skip to main content

Prediction of metalloproteinase family based on the concept of Chou’s pseudo amino acid composition using a machine learning approach

Overview of attention for article published in Journal of Structural and Functional Genomics, December 2011
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
71 Dimensions

Readers on

mendeley
10 Mendeley
Title
Prediction of metalloproteinase family based on the concept of Chou’s pseudo amino acid composition using a machine learning approach
Published in
Journal of Structural and Functional Genomics, December 2011
DOI 10.1007/s10969-011-9120-4
Pubmed ID
Authors

Majid Mohammad Beigi, Mohaddeseh Behjati, Hassan Mohabatkar

Abstract

Matrix metalloproteinase (MMPs) and disintegrin and metalloprotease (ADAMs) belong to the zinc-dependent metalloproteinase family of proteins. These proteins participate in various physiological and pathological states. Thus, prediction of these proteins using amino acid sequence would be helpful. We have developed a method to predict these proteins based on the features derived from Chou's pseudo amino acid composition (PseAAC) server and support vector machine (SVM) as a powerful machine learning approach. With this method, for ADAMs and MMPs families, an overall accuracy and Matthew's correlation coefficient (MCC) of 95.89 and 0.90% were achieved respectively. Furthermore, the method is able to predict two major subclasses of MMP family; Furin-activated secreted MMPs and Type II trans-membrane; with MCC of 0.89 and 0.91%, respectively. The overall accuracy for Furin-activated secreted MMPs and Type II trans-membrane was 98.18 and 99.07, respectively. Our data demonstrates an effective classification of Metalloproteinase family based on the concept of PseAAC and SVM.

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 1 10%
Unknown 9 90%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 30%
Student > Ph. D. Student 2 20%
Student > Postgraduate 1 10%
Student > Bachelor 1 10%
Lecturer > Senior Lecturer 1 10%
Other 2 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 50%
Computer Science 2 20%
Unspecified 1 10%
Chemistry 1 10%
Engineering 1 10%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 23 June 2013.
All research outputs
#3,692,195
of 12,661,990 outputs
Outputs from Journal of Structural and Functional Genomics
#22
of 95 outputs
Outputs of similar age
#79,908
of 277,881 outputs
Outputs of similar age from Journal of Structural and Functional Genomics
#2
of 2 outputs
Altmetric has tracked 12,661,990 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 95 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 38th percentile – i.e., 38% 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 277,881 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 67% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.