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Research Article
Dali Wang
Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University
Corresponding Author
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Background
Mutations in an enzyme target are one of the most common mechanisms whereby antibiotic resistance arises. Identification of the resistance mutations in bacteria is essential for understanding the structural basis of antibiotic resistance and design of new drugs. However, the traditionally used experimental approaches to identify resistance mutations were usually labor-intensive and costly.
Results
We present a machine learning (ML)-based classifier for predicting rifampicin (Rif) resistance mutations in bacterial RNA Polymerase subunit β (RpoB). A total of 66 resistance mutations were gathered from the literature to form positive dataset, while 53 residue variations of RpoB among a series of naturally occurring species were obtained as negative database. The features of the mutated RpoB and their binding energies with Rif were calculated through computational methods, and used as the mutation attributes for modelling. Classifiers based on four ML algorithms, i.e. decision tree, k nearest neighbors, naïve Bayes and supporting vector machine, were developed, which showed accuracy ranging from 0.69 to 0.76. A majority consensus approach was then used to obtain a new classifier based on the classifications of the four individual ML algorithms. The majority consensus classifier significantly improved the predictive performance, with accuracy, precision, recall and specificity of 0.83, 0.84, 0.86 and 0.83, respectively.
Conclusion
The majority consensus classifier provides an alternative methodology for rapid identification of resistance mutations in bacteria, which may help with early detection of antibiotic resistance and new drug discovery.
Keywords
Resistance mutation, machine learning, classifier, rifampicin, prediction
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CURRENT STATUS: Published
View the published article at BMC Bioinformatics.
Version 1
Posted 17 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 01 Feb, 2021
Reviews received
Received 30 Dec, 2020
Reviewers agreed
On 19 Dec, 2020
Reviewers agreed
On 17 Dec, 2020
Reviewers invited
Invitations sent on 16 Dec, 2020
Editor assigned
On 15 Dec, 2020
Editor invited
On 15 Dec, 2020
Submission checks complete
On 15 Dec, 2020
First submitted
On 11 Dec, 2020
Metrics
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Subject Areas
Bioinformatics
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A new preprint design is coming!
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See the published version of this preprint at BMC Bioinformatics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Dali Wang
Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Bioinformatics.
Version 1
Posted 17 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 01 Feb, 2021
Reviews received
Received 30 Dec, 2020
Reviewers agreed
On 19 Dec, 2020
Reviewers agreed
On 17 Dec, 2020
Reviewers invited
Invitations sent on 16 Dec, 2020
Editor assigned
On 15 Dec, 2020
Editor invited
On 15 Dec, 2020
Submission checks complete
On 15 Dec, 2020
First submitted
On 11 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Bioinformatics.
Background
Mutations in an enzyme target are one of the most common mechanisms whereby antibiotic resistance arises. Identification of the resistance mutations in bacteria is essential for understanding the structural basis of antibiotic resistance and design of new drugs. However, the traditionally used experimental approaches to identify resistance mutations were usually labor-intensive and costly.
Results
We present a machine learning (ML)-based classifier for predicting rifampicin (Rif) resistance mutations in bacterial RNA Polymerase subunit β (RpoB). A total of 66 resistance mutations were gathered from the literature to form positive dataset, while 53 residue variations of RpoB among a series of naturally occurring species were obtained as negative database. The features of the mutated RpoB and their binding energies with Rif were calculated through computational methods, and used as the mutation attributes for modelling. Classifiers based on four ML algorithms, i.e. decision tree, k nearest neighbors, naïve Bayes and supporting vector machine, were developed, which showed accuracy ranging from 0.69 to 0.76. A majority consensus approach was then used to obtain a new classifier based on the classifications of the four individual ML algorithms. The majority consensus classifier significantly improved the predictive performance, with accuracy, precision, recall and specificity of 0.83, 0.84, 0.86 and 0.83, respectively.
Conclusion
The majority consensus classifier provides an alternative methodology for rapid identification of resistance mutations in bacteria, which may help with early detection of antibiotic resistance and new drug discovery.
Figure 1
Figure 1
Figure 2
Figure 2
Figure 3
Figure 3
Figure 4
Figure 4
Figure 5
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
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