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Applied Biosciences
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Neural Networks and Deep Learning for Biosciences
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Neural Networks and Deep Learning for Biosciences

A special issue of Applied Biosciences (ISSN 2813-0464).

Deadline for manuscript submissions: closed (31 January 2026) | Viewed by 26421

Special Issue Editor

Dr. Nikolaos Kourkoumelis

E-Mail Website
Guest Editor
Department Medical Physics, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece
Interests: optical methods for tissue diagnostics; bio-molecular spectroscopy; X-ray diffraction; computational biophysics and drug design; molecular modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biosciences are becoming increasingly data-centric and data intensive. Diagnostics and related methodologies that once exclusively relied on experts to characterize cells, tissues, and medical information are now using big data computational techniques for decision making. Deep learning encompasses machine learning algorithms that combine a network of successive processing layers of data representation. Modern deep learning can expand to tens or hundreds of layers depending on the complexity of the raw data and the learning success of the layered representations. The whole process is achieved via models that are called neural networks, inspired by the processing of information in the brain.

Deep learning has shown remarkable success in numerous life sciences disciplines, but amid concerns for lack of biological context. Nevertheless, as the field of biosciences rapidly evolves, so do the data and the computational resources available to researchers. Thus, the emerging combination of deep learning with biosciences, although challenging, can lead to high-impact goals in healthcare analytics, biomedical diagnosis, research in biology (including biophysics and biochemistry), personalized medicine, and pharmaceutical development.

This Special Issue is open for innovative contributions related to the above-mentioned topics. Manuscripts discussing the ethical considerations of deep learning in healthcare are also welcome.

Dr. Nikolaos Kourkoumelis
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 Biosciences is an international peer-reviewed open access quarterly 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 1200 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

  • neural networks
  • deep learning
  • biomedical diagnosis
  • artificial intelligence in biosciences
  • image analysis
  • biomedical signal processing
  • precision medicine
  • omics
  • computer-aided drug design
  • healthcare data analytics

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Related Special Issue

Published Papers (7 papers)

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Research

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24 pages, 3009 KB  
Article
Classification of Apis cerana Populations Using Deep Learning Based on Morphometrics of Forewing in Thailand
by Nattawut Chumnoi, Papinwich Paimsang, Watcharaporn Cholamjiak and Tipwan Suppasat
Appl. Biosci. 2026, 5(1), 5; https://doi.org/10.3390/applbiosci5010005 - 20 Jan 2026
Viewed by 507
Abstract
This study aimed to develop a robust morphometric-based framework for classifying Apis cerana populations using deep learning and machine learning approaches. Previous studies on Apis cerana population differentiation have primarily relied on manual morphometrics or genetic markers, which are labor-intensive and often lack [...] Read more.
This study aimed to develop a robust morphometric-based framework for classifying Apis cerana populations using deep learning and machine learning approaches. Previous studies on Apis cerana population differentiation have primarily relied on manual morphometrics or genetic markers, which are labor-intensive and often lack scalability for large image-based datasets. Forewing landmarks were automatically detected through a deep learning model employing a heatmap regression and Hourglass Network architecture. The extracted coordinates were processed by Principal Component Analysis (PCA) for dimensionality reduction, and shape alignment was further refined through Procrustes ANOVA to minimize non-biological variation. Nine machine learning algorithms were trained and compared under identical preprocessing and validation settings. Among them, the Extra Trees classifier achieved the highest accuracy (99.7%) in distinguishing the three populations—A. cerana cerana from China and A. cerana indica from Thailand, the northern and southern populations. After applying error-based data filtering and retraining, classification accuracy improved further, with almost perfect population separation. The Procrustes ANOVA confirmed that individual variation significantly exceeded residual error (Pillai’s trace = 1.13, p < 0.0001), validating the biological basis of shape differences. Mahalanobis distance and permutation tests (10,000 rounds) revealed significant morphological divergence among populations (p < 0.0001). The integration of geometric alignment and ensemble learning demonstrated a highly reliable strategy for population identification, supporting morphometric and evolutionary studies in Apis cerana. Full article
(This article belongs to the Special Issue Neural Networks and Deep Learning for Biosciences)
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16 pages, 4102 KB  
Article
Performance of Radiologists in Characterizing and Diagnosing Hepatic Lesions Using Dynamic Contrast-Enhanced CT With and Without Artificial Intelligence
by Daiki Nishigaki, Atsushi Nakamoto, Takahiro Tsuboyama, Hiromitsu Onishi, Yuki Suzuki, Tomohiro Wataya, Kosuke Kita, Junya Sato, Miyuki Tomiyama, Masahiro Yanagawa, Masatoshi Hori, Shoji Kido and Noriyuki Tomiyama
Appl. Biosci. 2025, 4(4), 56; https://doi.org/10.3390/applbiosci4040056 - 3 Dec 2025
Viewed by 858
Abstract
Background: To investigate the performance of radiologists in characterizing and diagnosing hepatic lesions with and without the assistance of deep learning-based artificial intelligence (AI). Methods: This retrospective study included 83 nodules/masses from 69 patients who underwent dynamic contrast-enhanced CT of the liver. Image [...] Read more.
Background: To investigate the performance of radiologists in characterizing and diagnosing hepatic lesions with and without the assistance of deep learning-based artificial intelligence (AI). Methods: This retrospective study included 83 nodules/masses from 69 patients who underwent dynamic contrast-enhanced CT of the liver. Image assessments were conducted by 20 radiologists. grouped according to their level of experience (10 senior and 10 junior). Each radiologist determined the probability of eight characteristics based on enhancement patterns and the diagnosis with and without AI attached to the SYNAPSE SAI viewer (FUJIFILM Corporation, Minato-ku, Japan). The reference standard for comparison was established as follows: final diagnoses were based on pathology for 39 lesions and expert imaging consensus for the remainder, while image characteristics for all lesions were determined by expert imaging consensus. Areas under the receiver operating characteristic curves (AUCs) were analyzed using the multireader multicase method. Results: Using AI significantly improved the overall AUCs for both the characterization and the diagnosis of liver lesions. Improvement was suggested for specific items, including the characterization of enhancement, nonperipheral washout, and delayed enhancement, and the diagnosis of hepatocellular carcinoma. The utilization of AI system also suggested potential improvements in the AUCs for image characterization in both the senior and junior groups. Conclusions: Using AI improved the radiologists’ performance in characterizing and diagnosing hepatic lesions. In terms of their capacity to assess imaging characteristics, improvements were observed regardless of their level of experience. Full article
(This article belongs to the Special Issue Neural Networks and Deep Learning for Biosciences)
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23 pages, 6296 KB  
Article
Dynamic Patch-Based Sample Generation for Pulmonary Nodule Segmentation in Low-Dose CT Scans Using 3D Residual Networks for Lung Cancer Screening
by Ioannis D. Marinakis, Konstantinos Karampidis, Giorgos Papadourakis and Mostefa Kara
Appl. Biosci. 2025, 4(1), 14; https://doi.org/10.3390/applbiosci4010014 - 5 Mar 2025
Cited by 2 | Viewed by 2778
Abstract
Lung cancer is by far the leading cause of cancer death among both men and women, making up almost 25% of all cancer deaths Each year, more people die of lung cancer than colon, breast, and prostate cancer combined. The early detection of [...] Read more.
Lung cancer is by far the leading cause of cancer death among both men and women, making up almost 25% of all cancer deaths Each year, more people die of lung cancer than colon, breast, and prostate cancer combined. The early detection of lung cancer is critical for improving patient outcomes, and automation through advanced image analysis techniques can significantly assist radiologists. This paper presents the development and evaluation of a computer-aided diagnostic system for lung cancer screening, focusing on pulmonary nodule segmentation in low-dose CT images, by employing HighRes3DNet. HighRes3DNet is a specialized 3D convolutional neural network (CNN) architecture based on ResNet principles which uses residual connections to efficiently learn complex spatial features from 3D volumetric data. To address the challenges of processing large CT volumes, an efficient patch-based extraction pipeline was developed. This method dynamically extracts 3D patches during training with a probabilistic approach, prioritizing patches likely to contain nodules while maintaining diversity. Data augmentation techniques, including random flips, affine transformations, elastic deformations, and swaps, were applied in the 3D space to enhance the robustness of the training process and mitigate overfitting. Using a public low-dose CT dataset, this approach achieved a Dice coefficient of 82.65% on the testing set for 3D nodule segmentation, demonstrating precise and reliable predictions. The findings highlight the potential of this system to enhance efficiency and accuracy in lung cancer screening, providing a valuable tool to support radiologists in clinical decision-making. Full article
(This article belongs to the Special Issue Neural Networks and Deep Learning for Biosciences)
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15 pages, 6395 KB  
Article
Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset
by Muskan Naresh Jain, Salah Mohammed Awad Al-Heejawi, Jamil R. Azzi and Saeed Amal
Appl. Biosci. 2025, 4(1), 8; https://doi.org/10.3390/applbiosci4010008 - 5 Feb 2025
Cited by 6 | Viewed by 4511
Abstract
Kidney cancer has become a major global health issue over time, showing how early detection can play a very important role in mediating the disease. Traditional histological image analysis is recognized as the clinical gold standard for diagnosis, although it is highly manual [...] Read more.
Kidney cancer has become a major global health issue over time, showing how early detection can play a very important role in mediating the disease. Traditional histological image analysis is recognized as the clinical gold standard for diagnosis, although it is highly manual and labor-intensive. Due to this issue, many are interested in computer-aided diagnostic technologies to assist pathologists in their diagnostics. Specifically, deep learning (DL) has become a viable remedy in this field. Nonetheless, the capacity of existing DL models to extract comprehensive visual features for accurate classification is limited. Toward the end, this study proposes using ensemble models that combine the strengths of multiple transformers and deep learning model architectures. By leveraging the collective knowledge of these models, the ensemble enhances classification performance and enables more precise and effective kidney cancer detection. This study compares the performance of these suggested models to previous studies, all of which used the publicly accessible Dartmouth Kidney Cancer Histology Dataset. This study showed that the Vision Transformers, with an average accuracy of over 99%, were able to achieve high detection accuracy across all complete slide picture patches. In particular, the CAiT, DeiT, ViT, and Swin models outperformed ResNet. All things considered, the Vision Transformers consistently produced an average accuracy of 98.51% across all five-folds. These results demonstrated that Vision Transformers might perform well and successfully identify important features from smaller patches. Through utilizing histopathological images, our findings will assist pathologists in diagnosing kidney cancer, resulting in early detection and increased patient survival rates. Full article
(This article belongs to the Special Issue Neural Networks and Deep Learning for Biosciences)
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18 pages, 2058 KB  
Article
Multi-Criteria Decision Analysis in Drug Discovery
by Rafał A. Bachorz, Michael S. Lawless, David W. Miller and Jeremy O. Jones
Appl. Biosci. 2025, 4(1), 2; https://doi.org/10.3390/applbiosci4010002 - 6 Jan 2025
Cited by 2 | Viewed by 3851
Abstract
Drug discovery is inherently a multi-criteria optimization problem. In the first instance, it involves a tremendously large chemical space, where each compound can be characterized by multiple molecular and biological properties. Modern computational approaches try to efficiently explore the chemical space in search [...] Read more.
Drug discovery is inherently a multi-criteria optimization problem. In the first instance, it involves a tremendously large chemical space, where each compound can be characterized by multiple molecular and biological properties. Modern computational approaches try to efficiently explore the chemical space in search of molecules with the desired combination of properties. For example, Pareto optimizers identify a so-called “Pareto front”, a set of non-dominated solutions. From a qualitative perspective, all solutions on the front are potentially equally desirable, each expressing a trade-off between the goals. However, often there is a need to weight the objectives differently, depending on their perceived importance. To address this, we recently implemented a new Multi-Criteria Decision Analysis (MCDA) method as part of the AI-powered Drug Design (AIDDTM) technology initiative. This allows the user to weight various objective functions differently, which, in turn, efficiently directs the generative chemistry process toward the desired areas in chemical space. Full article
(This article belongs to the Special Issue Neural Networks and Deep Learning for Biosciences)
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Review

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26 pages, 770 KB  
Review
Artificial Intelligence in Reflectance Confocal Microscopy for Cutaneous Melanoma Computer-Assisted Detection: A Literature Review of Related Applications
by Luana Conte, Angela Filoni, Luca Schinzari, Ester Sofia Congedo, Lucia Pietroleonardo, Rocco Rizzo, Ugo De Giorgi, Donato Cascio, Giorgio De Nunzio and Maurizio Congedo
Appl. Biosci. 2026, 5(1), 20; https://doi.org/10.3390/applbiosci5010020 - 9 Mar 2026
Viewed by 396
Abstract
Cutaneous melanoma is one of the most aggressive skin cancers, and early diagnosis remains essential to reduce mortality. Reflectance Confocal Microscopy (RCM) provides non-invasive, quasi-histological images of the epidermis, dermoepidermal junction (DEJ), and dermis, enabling real-time assessment of melanocytic lesions. However, interpretation still [...] Read more.
Cutaneous melanoma is one of the most aggressive skin cancers, and early diagnosis remains essential to reduce mortality. Reflectance Confocal Microscopy (RCM) provides non-invasive, quasi-histological images of the epidermis, dermoepidermal junction (DEJ), and dermis, enabling real-time assessment of melanocytic lesions. However, interpretation still relies on expert visual evaluation, which is time-consuming and subjective. In this context, Artificial Intelligence (AI) and Computer-Assisted Detection (CAD) systems are emerging as valuable tools to improve diagnostic accuracy and reproducibility. This review summarizes research on AI applications in RCM imaging for melanoma, focusing on three major areas: delineation of skin strata, segmentation of tissues and morphological patterns, and classification of benign versus malignant lesions. Early approaches included Bayesian classifiers, wavelet-based decision trees, and logistic regression, while recent studies have employed support vector machines, random forests, and increasingly deep learning architectures such as convolutional and recurrent neural networks. The results demonstrate encouraging accuracy in DEJ localization, the segmentation of diagnostically relevant patterns, and the discrimination of melanoma from benign nevi. We distinguish the maturity of dermoscopy-based AI (AUC (ROC) > 0.80 on large multicenter cohorts) from the still-exploratory evidence for RCM-based AI. Nonetheless, current studies are often limited by small datasets, heterogeneous protocols, and a lack of multicenter validation. Overall, progress in AI applied to RCM supports the development of CAD systems that could assist clinicians during acquisition and diagnosis, reducing unnecessary biopsies and improving early melanoma detection. Future work should address standardization, dataset expansion, and the integration of advanced AI methods to move closer to clinical implementation. Full article
(This article belongs to the Special Issue Neural Networks and Deep Learning for Biosciences)
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32 pages, 1749 KB  
Review
A Review of Deep Learning Techniques for Leukemia Cancer Classification Based on Blood Smear Images
by Rakhmonalieva Farangis Oybek Kizi, Tagne Poupi Theodore Armand and Hee-Cheol Kim
Appl. Biosci. 2025, 4(1), 9; https://doi.org/10.3390/applbiosci4010009 - 5 Feb 2025
Cited by 22 | Viewed by 10833
Abstract
This research reviews deep learning methodologies for detecting leukemia, a critical cancer diagnosis and treatment aspect. Using a systematic mapping study (SMS) and systematic literature review (SLR), thirty articles published between 2019 and 2023 were analyzed to explore the advancements in deep learning [...] Read more.
This research reviews deep learning methodologies for detecting leukemia, a critical cancer diagnosis and treatment aspect. Using a systematic mapping study (SMS) and systematic literature review (SLR), thirty articles published between 2019 and 2023 were analyzed to explore the advancements in deep learning techniques for leukemia diagnosis using blood smear images. The analysis reveals that state-of-the-art models, such as Convolutional Neural Networks (CNNs), transfer learning, Vision Transformers (ViTs), ensemble methods, and hybrid models, achieved excellent classification accuracies. Preprocessing methods, including normalization, edge enhancement, and data augmentation, significantly improved model performance. Despite these advancements, challenges such as dataset limitations, the lack of model interpretability, and ethical concerns regarding data privacy and bias remain critical barriers to widespread adoption. The review highlights the need for diverse, well-annotated datasets and the development of explainable AI models to enhance clinical trust and usability. Additionally, addressing regulatory and integration challenges is essential for the safe deployment of these technologies in healthcare. This review aims to guide researchers in overcoming these challenges and advancing deep learning applications to improve leukemia diagnostics and patient outcomes. Full article
(This article belongs to the Special Issue Neural Networks and Deep Learning for Biosciences)
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