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Applied Sciences
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Artificial Intelligence in Bioinformatics: Current Status and Future Prospects
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Artificial Intelligence in Bioinformatics: Current Status and Future Prospects

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 6215

Special Issue Editors

Dr. Chao Yang

E-Mail Website
Guest Editor
Department of Chemistry, New York University, New York, NY 10003, USA
Interests: development of computational protocols for structure-based inhibitor design
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Zhou

E-Mail Website
Guest Editor
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
Interests: bioinformatics; computational peptides; drug design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue on “Artificial Intelligence in Bioinformatics: Current Status and Future Prospects”.

In recent years, artificial intelligence (AI) has received a great deal of attention in diverse research fields, and bioinformatics is no exception. With the rapid progress of computational power and exponential increase of data, AI has been applied in many branches of bioinformatics, such as biological/clinical data analysis and modeling, molecular structure prediction and structure-function analysis. The two major subsets of AI—machine learning (ML) and deep learning (DL)—have created a great deal of excitement in the research community. These methods can aid the interpretation or prediction of complex systems related to biology, chemistry, and pharmaceutical sciences, among others. Currently, the applications of ML/DL modeling in bioinformatics research are still in the preliminary stage; it would be valuable to understand how AI boosts performance in many aspects of bioinformatics research.

In this Special Issue, we invite submissions showing cutting-edge AI techniques in bioinformatics, comprehensive evaluations of the state-of-art ML/DL methods and dataset curation and their applications, as well as comprehensive reviews covering wide-ranging interests related to the current status, limitations, and future prospects of AI in bioinformatics. We hope that this Special Issue will be an open platform for researchers to share their knowledge, ideas and work.

The following keywords offer an indication of the topics invited, and are by no means limiting.

Dr. Chao Yang
Dr. Peng Zhou
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • deep learning
  • machine learning
  • artificial intelligence
  • bioinformatics
  • network-driven interpretation
  • big biodata
  • graph neural networks
  • AI-driven drug design
  • molecular structure prediction

Published Papers (3 papers)

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Research

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18 pages, 4779 KiB  
Article
Gene Association Classification for Autism Spectrum Disorder: Leveraging Gene Embedding and Differential Gene Expression Profiles to Identify Disease-Related Genes
by Apichat Suratanee and Kitiporn Plaimas
Appl. Sci. 2023, 13(15), 8980; https://doi.org/10.3390/app13158980 - 5 Aug 2023
Cited by 3 | Viewed by 1472
Abstract
Identifying genes associated with autism spectrum disorder (ASD) is crucial for understanding the underlying mechanisms of the disorder. However, ASD is a complex condition involving multiple mechanisms, and this has resulted in an unclear understanding of the disease and a lack of precise [...] Read more.
Identifying genes associated with autism spectrum disorder (ASD) is crucial for understanding the underlying mechanisms of the disorder. However, ASD is a complex condition involving multiple mechanisms, and this has resulted in an unclear understanding of the disease and a lack of precise knowledge concerning the genes associated with ASD. To address these challenges, we conducted a systematic analysis that integrated multiple data sources, including associations among ASD-associated genes and gene expression data from ASD studies. With these data, we generated both a gene embedding profile that captured the complex relationships between genes and a differential gene expression profile (built from the gene expression data). We utilized the XGBoost classifier and leveraged these profiles to identify novel ASD associations. This approach revealed 10,848 potential gene–gene associations and inferred 125 candidate genes, with DNA Topoisomerase I, ATP Synthase F1 Subunit Gamma, and Neuronal Calcium Sensor 1 being the top three candidates. We conducted a statistical analysis to assess the relevance of candidate genes to specific functions and pathways. Additionally, we identified sub-networks within the candidate network to uncover sub-groups of associations that could facilitate the identification of potential ASD-related genes. Overall, our systematic analysis, which integrated multiple data sources, represents a significant step towards unraveling the complexities of ASD. By combining network-based gene associations, gene expression data, and machine learning, we contribute to ASD research and facilitate the discovery of new targets for molecularly targeted therapies. Full article
(This article belongs to the Special Issue Artificial Intelligence in Bioinformatics: Current Status and Future Prospects)
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17 pages, 3757 KiB  
Article
Predictive Assessment of Mycological State of Bulk-Stored Barley Using B-Splines in Conjunction with Genetic Algorithms
by Jolanta Wawrzyniak
Appl. Sci. 2023, 13(9), 5264; https://doi.org/10.3390/app13095264 - 23 Apr 2023
Cited by 1 | Viewed by 1042
Abstract
Postharvest grain preservation and storage can significantly affect the safety and nutritional value of cereal-based products. Negligence at this stage of the food processing chain can lead to mold development and mycotoxin accumulation, which pose considerable threats to the quality of harvested grain [...] Read more.
Postharvest grain preservation and storage can significantly affect the safety and nutritional value of cereal-based products. Negligence at this stage of the food processing chain can lead to mold development and mycotoxin accumulation, which pose considerable threats to the quality of harvested grain and, thus, to consumer health. Predictive models evaluating the risk associated with fungal activity constitute a promising solution for decision-making modules in advanced preservation management systems. In this study, an attempt was made to combine genetic algorithms and B-spline curves in order to develop a predictive model to assess the mycological state of malting barley grain stored at various temperatures (T = 12–30 °C) and water activity in grain (aw = 0.78–0.96). It was found that the B-spline curves consisting of four second-order polynomials were sufficient to approximate the datasets describing fungal growth in barley ecosystems stored under steady temperature and humidity conditions. Based on the designated structures of B-spline curves, a universal parameterized model covering the entire range of tested conditions was developed. In the model, the coordinates of the control points of B-spline curves were modulated by genetic algorithms using values of storage parameters (aw and T). A statistical assessment of model performance showed its high efficiency (R2 = 0.94, MAE = 0.21, RMSE = 0.28). As the proposed model is based on easily measurable on-line storage parameters, it could be used as an effective tool supporting modern systems of postharvest grain treatment. Full article
(This article belongs to the Special Issue Artificial Intelligence in Bioinformatics: Current Status and Future Prospects)
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Review

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23 pages, 1997 KiB  
Review
Artificial Intelligence (AI)-Based Systems for Automatic Skeletal Maturity Assessment through Bone and Teeth Analysis: A Revolution in the Radiological Workflow?
by Elena Caloro, Maurizio Cè, Daniele Gibelli, Andrea Palamenghi, Carlo Martinenghi, Giancarlo Oliva and Michaela Cellina
Appl. Sci. 2023, 13(6), 3860; https://doi.org/10.3390/app13063860 - 17 Mar 2023
Cited by 3 | Viewed by 2741
Abstract
Bone age is an indicator of bone maturity and is useful for the treatment of different pediatric conditions as well as for legal issues. Bone age can be assessed by the analysis of different skeletal segments and teeth and through several methods; however, [...] Read more.
Bone age is an indicator of bone maturity and is useful for the treatment of different pediatric conditions as well as for legal issues. Bone age can be assessed by the analysis of different skeletal segments and teeth and through several methods; however, traditional bone age assessment is a complicated and time-consuming process, prone to inter- and intra-observer variability. There is a high demand for fully automated systems, but creating an accurate and reliable solution has proven difficult. Deep learning technology, machine learning, and Convolutional Neural Networks-based systems, which are rapidly evolving, have shown promising results in automated bone age assessment. We provide the background of bone age estimation, its usefulness and traditional methods of assessment, and review the currently artificial-intelligence-based solutions for bone age assessment and the future perspectives of these applications. Full article
(This article belongs to the Special Issue Artificial Intelligence in Bioinformatics: Current Status and Future Prospects)
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