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Artificial Intelligence Applications in Genetics and Genomics
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Artificial Intelligence Applications in Genetics and Genomics

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 8480

Special Issue Editor

Dr. Denis Bauer

E-Mail Website
Guest Editor
1. Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, North Ryde 3169, Australia
2. Department of Biomedical Sciences, Faculty of Medicine and Health Science, Macquarie University, Macquarie Park 2109, Australia
3. Applied BioSciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park 2109, Australia
Interests: machine learning; big genomic data; artificial intelligence; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This open access Special Issue on “Artificial Intelligence Applications in Genetics and Genomics” enables us to celebrate the computer science advancements that have greatly accelerated our gaining insights into genomic function.

We welcome reviews, original articles, and short communications covering the development or application of machine learning (ML) and artificial intelligence (AI) for genetic or genomic data analysis. In particular, we are seeking showcases where traditional approaches fell short either in gaining insights or processing the data and subsequently new ML or AI approaches had to be developed. Data analysis domains may include omics data analysis (genomics, transcriptomics, methylomics), ML/AI-based clinical or agricultural association studies for diseases or traits, genome engineering or synthetic biology applications, as well as biological feature prediction or sample classification approaches.

This Special Issue showcases trailblazing software and innovative analytics to inspire the increased use of ML and AI in genetics and genomics. We look forward to your contributions and are happy to offer support around topic clarification or enabling reproducible research practices (e.g., through cloud usage).

Dr. Denis Bauer
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. Genes 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 2600 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
  • Computational biology
  • Machine learning
  • Artificial intelligence
  • Omics
  • Genome engineering
  • Association studies
  • Clinical studies
  • Predictions
  • Classification

Published Papers (2 papers)

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Research

13 pages, 1559 KiB  
Article
i4mC-Deep: An Intelligent Predictor of N4-Methylcytosine Sites Using a Deep Learning Approach with Chemical Properties
by Waleed Alam, Hilal Tayara and Kil To Chong
Genes 2021, 12(8), 1117; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12081117 - 23 Jul 2021
Cited by 13 | Viewed by 2283
Abstract
DNA is subject to epigenetic modification by the molecule N4-methylcytosine (4mC). N4-methylcytosine plays a crucial role in DNA repair and replication, protects host DNA from degradation, and regulates DNA expression. However, though current experimental techniques can identify 4mC sites, such techniques are expensive [...] Read more.
DNA is subject to epigenetic modification by the molecule N4-methylcytosine (4mC). N4-methylcytosine plays a crucial role in DNA repair and replication, protects host DNA from degradation, and regulates DNA expression. However, though current experimental techniques can identify 4mC sites, such techniques are expensive and laborious. Therefore, computational tools that can predict 4mC sites would be very useful for understanding the biological mechanism of this vital type of DNA modification. Conventional machine-learning-based methods rely on hand-crafted features, but the new method saves time and computational cost by making use of learned features instead. In this study, we propose i4mC-Deep, an intelligent predictor based on a convolutional neural network (CNN) that predicts 4mC modification sites in DNA samples. The CNN is capable of automatically extracting important features from input samples during training. Nucleotide chemical properties and nucleotide density, which together represent a DNA sequence, act as CNN input data. The outcome of the proposed method outperforms several state-of-the-art predictors. When i4mC-Deep was used to analyze G. subterruneus DNA, the accuracy of the results was improved by 3.9% and MCC increased by 10.5% compared to a conventional predictor. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Genetics and Genomics)
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14 pages, 513 KiB  
Article
UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites
by Arslan Siraj, Dae Yeong Lim, Hilal Tayara and Kil To Chong
Genes 2021, 12(5), 717; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12050717 - 11 May 2021
Cited by 15 | Viewed by 3578
Abstract
Protein ubiquitylation is an essential post-translational modification process that performs a critical role in a wide range of biological functions, even a degenerative role in certain diseases, and is consequently used as a promising target for the treatment of various diseases. Owing to [...] Read more.
Protein ubiquitylation is an essential post-translational modification process that performs a critical role in a wide range of biological functions, even a degenerative role in certain diseases, and is consequently used as a promising target for the treatment of various diseases. Owing to the significant role of protein ubiquitylation, these sites can be identified by enzymatic approaches, mass spectrometry analysis, and combinations of multidimensional liquid chromatography and tandem mass spectrometry. However, these large-scale experimental screening techniques are time consuming, expensive, and laborious. To overcome the drawbacks of experimental methods, machine learning and deep learning-based predictors were considered for prediction in a timely and cost-effective manner. In the literature, several computational predictors have been published across species; however, predictors are species-specific because of the unclear patterns in different species. In this study, we proposed a novel approach for predicting plant ubiquitylation sites using a hybrid deep learning model by utilizing convolutional neural network and long short-term memory. The proposed method uses the actual protein sequence and physicochemical properties as inputs to the model and provides more robust predictions. The proposed predictor achieved the best result with accuracy values of 80% and 81% and F-scores of 79% and 82% on the 10-fold cross-validation and an independent dataset, respectively. Moreover, we also compared the testing of the independent dataset with popular ubiquitylation predictors; the results demonstrate that our model significantly outperforms the other methods in prediction classification results. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Genetics and Genomics)
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