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Microorganisms
Special Issues
Bioinformatics and Omic Data Analysis in Microbial Research
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Bioinformatics and Omic Data Analysis in Microbial Research

A special issue of Microorganisms (ISSN 2076-2607). This special issue belongs to the section "Systems Microbiology".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 486

Special Issue Editor

Prof. Dr. Douglas D. Rhoads

E-Mail Website
Guest Editor
1. Director of Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, AR 72701, USA
2. Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72701, USA
Interests: genomics; molecular genetics in eukaryotes and prokaryotes; bacterial diseases affecting poultry.

Special Issue Information

Dear Colleagues,

This Special Issue explores the current applications of omics to understand the interplay/function of microbes in ecology, agriculture, disease, homeostasis, and industry. We solicit papers covering new research or reviews related to software development, pipeline development, or application of bioinformatics to address important current topics concerning microorganisms. Papers may focus on any group of microorganisms including viruses, bacteria, fungi, protists, etc., using bioinformatics to decipher biologically relevant understandings through genomics, proteomics, metabolomics, transcriptomics, or any combination. Given the important biological roles of microorganisms, the application of omics in these organisms is critical to elicit a deeper understanding. Relevant areas of interest might include speciation and species complexes, and pan genome analyses including gene transfer, gene networks, microbiomes, symbiosis and pathogenesis.

Prof. Dr. Douglas D. Rhoads
Guest Editor

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. Microorganisms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). 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

  • bioinformatics
  • microorganisms
  • genomics
  • proteomics
  • metabolomics
  • software pipelines

Published Papers (1 paper)

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Research

18 pages, 1404 KiB  
Article
Exploring Pathogen Presence Prediction in Pastured Poultry Farms through Transformer-Based Models and Attention Mechanism Explainability
by Athish Ram Das, Nisha Pillai, Bindu Nanduri, Michael J. Rothrock, Jr. and Mahalingam Ramkumar
Microorganisms 2024, 12(7), 1274; https://0-doi-org.brum.beds.ac.uk/10.3390/microorganisms12071274 - 23 Jun 2024
Viewed by 205
Abstract
In this study, we explore how transformer models, which are known for their attention mechanisms, can improve pathogen prediction in pastured poultry farming. By combining farm management practices with microbiome data, our model outperforms traditional prediction methods in terms of the F1 score—an [...] Read more.
In this study, we explore how transformer models, which are known for their attention mechanisms, can improve pathogen prediction in pastured poultry farming. By combining farm management practices with microbiome data, our model outperforms traditional prediction methods in terms of the F1 score—an evaluation metric for model performance—thus fulfilling an essential need in predictive microbiology. Additionally, the emphasis is on making our model’s predictions explainable. We introduce a novel approach for identifying feature importance using the model’s attention matrix and the PageRank algorithm, offering insights that enhance our comprehension of established techniques such as DeepLIFT. Our results showcase the efficacy of transformer models in pathogen prediction for food safety and mark a noteworthy contribution to the progress of explainable AI within the biomedical sciences. This study sheds light on the impact of effective farm management practices and highlights the importance of technological advancements in ensuring food safety. Full article
(This article belongs to the Special Issue Bioinformatics and Omic Data Analysis in Microbial Research)
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