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Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach

Overview of attention for article published in BMC Genomics, September 2018
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Title
Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach
Published in
BMC Genomics, September 2018
DOI 10.1186/s12864-018-5030-1
Pubmed ID
Authors

Jesus A. Beltran, Longendri Aguilera-Mendoza, Carlos A. Brizuela

Abstract

Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model's performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed. Unfortunately, how to adapt this method to the selection of molecular descriptors for the classification of antimicrobial peptides and the performance it can achieve, have only preliminary been explored. We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The evaluation is conducted on six high-quality benchmark datasets that have previously been used for the empirical evaluation of state-of-art antimicrobial prediction tools in an unbiased manner. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform state-of-art prediction tools for the classification of antimicrobial and antibacterial peptides. The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. One of our future directions is aimed at using the obtained classifier to search for antimicrobial peptides in various transcriptomes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 23%
Student > Ph. D. Student 6 14%
Student > Bachelor 4 9%
Professor 4 9%
Researcher 4 9%
Other 5 12%
Unknown 10 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 28%
Computer Science 7 16%
Chemistry 6 14%
Medicine and Dentistry 4 9%
Agricultural and Biological Sciences 3 7%
Other 1 2%
Unknown 10 23%
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 27 September 2018.
All research outputs
#20,535,139
of 23,105,443 outputs
Outputs from BMC Genomics
#9,331
of 10,709 outputs
Outputs of similar age
#296,205
of 340,828 outputs
Outputs of similar age from BMC Genomics
#161
of 192 outputs
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