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Applied Sciences
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Application of Artificial Intelligence in Bioinformatics
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Application of Artificial Intelligence in Bioinformatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 521

Special Issue Editors

Dr. Arzu Tugce Guler

E-Mail Website
Guest Editor
Institute for Experiential AI, Northeastern University, Boston, MA 02115, USA
Interests: mass spectrometry; proteomics; metabolomics; multi-omics integration; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Ozkan Cigdem

E-Mail Website
Guest Editor
NYU Langone Health, New York University, New York, NY 10016, USA
Interests: deep learning; biomedical imaging; Parkinson’s disease; total knee replacement surgery
Prof. Dr. Malik Yousef

E-Mail Website
Guest Editor
Department of Information Systems, Zefat Academic College, Zefat 13206, Israel
Interests: bioinformatics; text mining; biological domain knowledge-based feature selection on gene expression data; microRNA; one-class classification
Special Issues, Collections and Topics in MDPI journals
Dr. Jeong Seop Sim

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Inha University, Incheon 22212, Republic of Korea
Interests: algorithm; bioinformatics; data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of artificial intelligence (AI) methods in bioinformatics is revolutionizing the field by enabling the analysis and interpretation of complex biological data at unprecedented scales, depth, and throughout. The ever-increasing utilizations of AI are driving advancements in clinical care, personalized medicine, drug discovery, and the understanding of fundamental biological processes.

Bioinformatics serves as a common denominator across various biological domains for addressing challenges in the analysis of complex data. This Special Issue aims to foster innovation and collaboration across these different domains by highlighting the latest developments in applications of AI methods in bioinformatics.

Topics of interest include, but are not limited to, the following:

  • AI in genomics, transcriptomics, proteomics, and metabolomics;
  • AI in the interpretation of high-throughput data;
  • AI-driven drug discovery and development;
  • AI in personalized medicine;
  • AI for clinical prediction and decision support systems;
  • AI in systems biology;
  • AI in biomedical image processing.

Dr. Arzu Tugce Guler
Dr. Ozkan Cigdem
Prof. Dr. Malik Yousef
Dr. Jeong Seop Sim
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

  • AI
  • drug discovery
  • personalized medicine
  • biomedical imaging
  • systems biology
  • omics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

12 pages, 921 KiB  
Article
Comparison of ECG Between Gameplay and Seated Rest: Machine Learning-Based Classification
by Emi Yuda, Hiroyuki Edamatsu, Yutaka Yoshida and Takahiro Ueno
Appl. Sci. 2025, 15(10), 5783; https://doi.org/10.3390/app15105783 - 21 May 2025
Abstract
The influence of gameplay on autonomic nervous system activity was investigated by comparing electrocardiogram (ECG) data during seated rest and gameplay. A total of 13 participants (6 in the gameplay group and 7 in the control group) were analyzed. RR interval time series [...] Read more.
The influence of gameplay on autonomic nervous system activity was investigated by comparing electrocardiogram (ECG) data during seated rest and gameplay. A total of 13 participants (6 in the gameplay group and 7 in the control group) were analyzed. RR interval time series (2 Hz) and heart-rate variability (HRV) indices, including mean RR, SDRR, VLF, LF, HF, LF/HF, and HF peak frequency, were extracted from ECG signals over 5 min and 10 min segments. HRV indices were calculated using fast Fourier transform (FFT). The classification was performed using Logistic Regression (LGR), Random Forest (RF), XGBoost (XGB, v2.9.2), One-Class SVM (OCS), Isolation Forest (ILF), and Local Outlier Factor (LOF). A balanced dataset of 5 min and 10 min segments was evaluated using k-fold cross-validation (k = 3, 4, 5). Performance metrics, including recall, F-score, and PR-AUC, were computed for each classifier. Grid search was applied to optimize parameters for LGR, RF, and XGB, while default settings were used for the other classifiers. Among all models, OCS with k = 3 achieved the highest classification accuracy for both 5 min and 10 min data. These findings suggest that machine learning-based classification can effectively distinguish ECG patterns between gameplay and rest. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Bioinformatics)
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12 pages, 607 KiB  
Article
Individual Differences in Sustained Attention: Effects of Age, Sex, and Time of Day Based on Psychomotor Vigilance Task Performance
by Emi Yuda and Yutaka Yoshida
Appl. Sci. 2025, 15(10), 5487; https://doi.org/10.3390/app15105487 - 14 May 2025
Viewed by 200
Abstract
Sustained attention is a critical cognitive function, especially in contexts such as driving safety, where performance deterioration due to fatigue or drowsiness can have serious consequences. Although individual differences in sustained attention have been recognized and are known to decline with age, quantitative [...] Read more.
Sustained attention is a critical cognitive function, especially in contexts such as driving safety, where performance deterioration due to fatigue or drowsiness can have serious consequences. Although individual differences in sustained attention have been recognized and are known to decline with age, quantitative analyses considering sex and circadian timing are limited. In this study, we analyzed psychomotor vigilance task (PVT) data from 356 participants to investigate the effects of age, sex, and time of day on attention performance. Participants completed a 5-min PVT, and metrics including the reaction time (RT), minor lapses (MNLs, ≥5 ms), major lapses (MJLs, ≥8 ms), and false starts (FSs) were calculated. A general linear model was applied with sex and testing time (8:00–12:00, 13:00–16:00, 16:00–18:00) as fixed factors and age as a covariate. Stepwise multiple regression was also used to assess how well age, sex, and time of day explain performance. The RT showed significant differences by time and age (p < 0.001), with higher numbers of MNLs during morning sessions. Both sexes demonstrated significantly better performance in the afternoon compared to the morning. These results highlight the importance of controlling for the testing time in PVT-based experiments and underscore the measurable individual differences in sustained attention. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Bioinformatics)
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