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Microbioinformatics
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Microbioinformatics

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 3600

Special Issue Editors

Dr. Hiromi Nishida

E-Mail Website
Guest Editor
Department of Biotechnology, Toyama Prefectural University, 5180 Kurokawa, Imizu, Toyama 939-0398, Japan
Interests: microbial evolution; cell enlargement; microinjection of long DNA into bacterial cell; cell-cell communication; genome evolution; microbioinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Hiroshi Toda

E-Mail Website
Guest Editor
Department of Biotechnology, Toyama Prefectural University, Toyama, Japan
Interests: microbiology; molecular biology; enzymology; metabolic engineering

Special Issue Information

Dear Colleagues,

We spend a lot of time working on computers in microbiology research. We do not perform computer programming but search nucleotide or amino acid sequences similar to our query sequences in an international DNA/protein database, predict RNA or protein structures, construct multiple alignment of nucleotide or amino acid sequences, construct phylogenetic tree based on the multiple alignment, perform gene annotation of the genome sequence that we determined, etc. It is almost impossible to find scientific original papers in microbiology that do not use a computer at all. Computer works are essential in microbiology, and they are not limited in omics analyses (genomics, transcriptomics, proteomics, etc.). How do you incorporate computer operations and programs into microbiology? In this Special Issue, the editors would like you to submit any papers of developing microbiology by utilizing computer works.

Dr. Hiromi Nishida
Dr. Hiroshi Toda
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • microbiology
  • bioinformatics
  • omics
  • computer work
  • bacteriology
  • mycology
  • microbial interaction
  • microbial evolution
  • microbial ecology
  • food microbiology

Published Papers (1 paper)

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Research

12 pages, 967 KiB  
Article
Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction
by Peter Májek, Lukas Lüftinger, Stephan Beisken, Thomas Rattei and Arne Materna
Int. J. Mol. Sci. 2021, 22(23), 13049; https://doi.org/10.3390/ijms222313049 - 2 Dec 2021
Cited by 8 | Viewed by 2560
Abstract
The prediction of antimicrobial resistance (AMR) based on genomic information can improve patient outcomes. Genetic mechanisms have been shown to explain AMR with accuracies in line with standard microbiology laboratory testing. To translate genetic mechanisms into phenotypic AMR, machine learning has been successfully [...] Read more.
The prediction of antimicrobial resistance (AMR) based on genomic information can improve patient outcomes. Genetic mechanisms have been shown to explain AMR with accuracies in line with standard microbiology laboratory testing. To translate genetic mechanisms into phenotypic AMR, machine learning has been successfully applied. AMR machine learning models typically use nucleotide k-mer counts to represent genomic sequences. While k-mer representation efficiently captures sequence variation, it also results in high-dimensional and sparse data. With limited training data available, achieving acceptable model performance or model interpretability is challenging. In this study, we explore the utility of feature engineering with several biologically relevant signals. We propose to predict the functional impact of observed mutations with PROVEAN to use the predicted impact as a new feature for each protein in an organism’s proteome. The addition of the new features was tested on a total of 19,521 isolates across nine clinically relevant pathogens and 30 different antibiotics. The new features significantly improved the predictive performance of trained AMR models for Pseudomonas aeruginosa, Citrobacter freundii, and Escherichia coli. The balanced accuracy of the respective models of those three pathogens improved by 6.0% on average. Full article
(This article belongs to the Special Issue Microbioinformatics)
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