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Applied Sciences
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Advanced Intelligent Technologies in Bioinformatics and Biomedicine
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Advanced Intelligent Technologies in Bioinformatics and Biomedicine

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

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 2786

Special Issue Editor

Prof. Dr. Young-Rae Cho

E-Mail Website
Guest Editor
Department of Software, Yonsei University, Mirae Campus, Wonju 26493, Republic of Korea
Interests: bioinformatics; network biology; systems biology; data mining; ontologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the intersection of advanced intelligent technologies and bioinformatics has revolutionized the landscape of biomedical research. The combination of artificial intelligence, machine learning, and big data analytics techniques has provided innovative approaches to analyzing biomedical data, understanding disease mechanisms, advancing personalized medicine, and accelerating drug discovery. As this field continues to evolve rapidly, there is a growing need to showcase the latest advancements and breakthroughs in this interdisciplinary domain.

This Special Issue, "Advanced Intelligent Technologies in Bioinformatics and Biomedicine", aims to provide a platform for researchers to disseminate their cutting-edge research findings, methodologies, and applications in the field of bioinformatics and biomedicine. Researchers are invited to contribute original research articles and review articles to this Special Issue. This collection of works will serve as a valuable resource for researchers and practitioners, contributing to further advancements in the field.

Prof. Dr. Young-Rae Cho
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 250 words) can be sent to the Editorial Office for assessment.

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 approaches to biomedical data analysis
  • computational genomics, proteomics, and metabolomics
  • single-cell omics data analysis
  • network biology and systems biology
  • biomarker discovery and personalized medicine
  • pharmacogenomics and drug discovery
  • AI-driven medical imaging and diagnostics
  • bioinformatics tools and software development

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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

28 pages, 13960 KB  
Article
Deep Learning Approaches for Brain Tumor Classification in MRI Scans: An Analysis of Model Interpretability
by Emanuela F. Gomes and Ramiro S. Barbosa
Appl. Sci. 2026, 16(2), 831; https://doi.org/10.3390/app16020831 - 14 Jan 2026
Cited by 2 | Viewed by 1622
Abstract
This work presents the development and evaluation of Artificial Intelligence (AI) models for the automatic classification of brain tumors in Magnetic Resonance Imaging (MRI) scans. Several deep learning architectures were implemented and compared, including VGG-19, ResNet50, EfficientNetB3, Xception, MobileNetV2, DenseNet201, InceptionV3, Vision Transformer [...] Read more.
This work presents the development and evaluation of Artificial Intelligence (AI) models for the automatic classification of brain tumors in Magnetic Resonance Imaging (MRI) scans. Several deep learning architectures were implemented and compared, including VGG-19, ResNet50, EfficientNetB3, Xception, MobileNetV2, DenseNet201, InceptionV3, Vision Transformer (ViT), and an Ensemble model. The models were developed in Python (version 3.12.4) using the Keras and TensorFlow frameworks and trained on a public Brain Tumor MRI dataset containing 7023 images. Data augmentation and hyperparameter optimization techniques were applied to improve model generalization. The results showed high classification performance, with accuracies ranging from 89.47% to 98.17%. The Vision Transformer achieved the best performance, reaching 98.17% accuracy, outperforming traditional Convolutional Neural Network (CNN) architectures. Explainable AI (XAI) methods Grad-CAM, LIME, and Occlusion Sensitivity were employed to assess model interpretability, showing that the models predominantly focused on tumor regions. The proposed approach demonstrated the effectiveness of AI-based systems in supporting early diagnosis of brain tumors, reducing analysis time and assisting healthcare professionals. Full article
(This article belongs to the Special Issue Advanced Intelligent Technologies in Bioinformatics and Biomedicine)
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18 pages, 2038 KB  
Article
Optimal Cell Segmentation and Counting Strategy for Embedding in Low-Power AIoT Devices
by Gunwoo Park, Junmin Park and Sungjin Lee
Appl. Sci. 2026, 16(1), 357; https://doi.org/10.3390/app16010357 - 29 Dec 2025
Viewed by 544
Abstract
This study proposes an end-to-end (E2E) optimization methodology for a white blood cell (WBC) cell segmentation and counting (CSC) pipeline with a focus on deployment to low-power Artificial Intelligence of Things (AIoT) devices. The proposed framework addresses not only the selection of the [...] Read more.
This study proposes an end-to-end (E2E) optimization methodology for a white blood cell (WBC) cell segmentation and counting (CSC) pipeline with a focus on deployment to low-power Artificial Intelligence of Things (AIoT) devices. The proposed framework addresses not only the selection of the segmentation model but also the corresponding loss function design, watershed threshold optimization for cell counting, and model compression strategies to balance accuracy, latency, and model size in embedded AIoT applications. For segmentation model selection, UNet, UNet++, ResUNet, EffUNet, FPN, BiFPN, PFPN, Cell-ViT, Evit-UNet and MAXVitUNet were employed, and three types of loss functions—binary cross-entropy (BCE), focal loss, and Dice loss—were utilized for model training. For cell-counting accuracy optimization, a distance transform-based watershed algorithm was applied, and the optimal threshold value was determined experimentally to lie within the range of 0.4 to 0.9. Quantization and pruning techniques were also considered for model compression. Experimental results demonstrate that using an FPN model trained with focal loss and setting the watershed threshold to 0.65 yields the optimal configuration. Compared to the latest baseline techniques, the proposed CSC E2E pipeline achieves a 21.1% improvement in cell-counting accuracy while reducing model size by 74.5% and latency by 16.8% through model compression. These findings verify the effectiveness of the proposed optimization strategy as a lightweight and efficient solution for real-time biomedical applications on low-power AIoT devices. Full article
(This article belongs to the Special Issue Advanced Intelligent Technologies in Bioinformatics and Biomedicine)
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