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Biology
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AI Deep Learning Approach to Study Biological Questions (2nd Edition)
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AI Deep Learning Approach to Study Biological Questions (2nd Edition)

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 2731

Special Issue Editors

Prof. Dr. Chung-Der Hsiao

E-Mail Website
Guest Editor
Epidermal Stem Cell Lab, Department of Bioscience Technology, Chung Yuan Christian University, Chung-Li 320314, Taiwan
Interests: deep learning; image analysis; aquatic animal physiology and toxicology; new tool invention
Special Issues, Collections and Topics in MDPI journals
Dr. Tzong-Rong Ger

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Chung Yuan Christian University, Chung-Li 320314, Taiwan
Interests: artificial intelligent; medical image analysis; bio-signal analysis; biosensor; smart medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the help of computer calculation power, we are witnessing a transition from the manual to the fully automated and systematic dissection of biological questions. Some research fields, such as developmental biology, molecular biology, physiology, ecology, taxonomy, etc., have undergone considerable advancements with the aid of AI deep learning. For example, daphnia and zebrafish are two important aquatic animals used in developmental and toxicological studies. Using the U-Net/Mask RCNN deep learning and machine vision OpenCV approaches, we were able to address cardiac physiology alterations following exposure to environmental pollutants [1,2]. In tetrahymena, we were able to conduct precise cell quantification using the deep-learning-based StarDist tool [3]. This Special Issue of Biology particularly welcomes researchers who use deep learning or machine vision to address diverse biological questions relating to fundamental, biomedical and other relevant fields. Image segmentation, classification, locomotion trajectory analysis and volumetric prediction applied to plants, animals or protozoa are especially welcome. Research into novel algorithms or new applications that can aid wet-lab biological researchers in asking better biological questions will be appreciated. This Special Issue of Biology invites researchers and clinicians worldwide to submit their results or reviews within the scope of the title.

[1] Saputra, F.; Farhan, A.; Suryanto, M.E.; Kurnia, K.A.; Chen, K.H.-C.; Vasquez, R.D.; Roldan, M.J.M.; Huang, J.-C.; Lin, Y.-K.; Hsiao, C.-D. Automated Cardiac Chamber Size and Cardiac Physiology Measurement in Water Fleas by U-Net and Mask RCNN Convolutional Networks. Animals 2022, 12, 1670. https://doi.org/10.3390/ani12131670

[2] Farhan, A.; Kurnia, K.A.; Saputra, F.; Chen, K.H.-C.; Huang, J.-C.; Roldan, M.J.M.; Lai, Y.-H.; Hsiao, C.-D. An OpenCV-Based Approach for Automated Cardiac Rhythm Measurement in Zebrafish from Video Datasets. Biomolecules 2021, 11, 1476. https://doi.org/10.3390/biom11101476

[3] Kurnia, K.A.; Sampurna, B.P.; Audira, G.; Juniardi, S.; Vasquez, R.D.; Roldan, M.J.M.; Tsao, C.-C.; Hsiao, C.-D. Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension. Int. J. Mol. Sci. 2022, 23, 6009. https://doi.org/10.3390/ijms23116009

Prof. Dr. Chung-Der Hsiao
Dr. Tzong-Rong Ger
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. Biology 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 2700 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

  • OpenCV
  • Mask RCNN
  • YOLO
  • U-Net
  • StarDist
  • ImageJ
  • MATLAB
  • image segmentation
  • image classification
  • locomotion trajectory analysis
  • volumetric prediction
  • plants
  • animals
  • protozoa
  • invertebrates
  • animal behavior
  • developmental biology
  • toxicology
  • zebrafish
  • medaka
  • daphnia

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Related Special Issue

Published Papers (3 papers)

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45 pages, 65600 KiB  
Article
scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data
by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang and Shilei Chen
Biology 2025, 14(6), 679; https://doi.org/10.3390/biology14060679 - 11 Jun 2025
Abstract
Single-cell RNA sequencing now profiles whole transcriptomes for hundreds of thousands of cells, yet existing trajectory-inference tools rarely pinpoint where and when fate decisions are made. We present single-cell reinforcement learning (scRL), an actor–critic framework that recasts differentiation as a sequential decision process [...] Read more.
Single-cell RNA sequencing now profiles whole transcriptomes for hundreds of thousands of cells, yet existing trajectory-inference tools rarely pinpoint where and when fate decisions are made. We present single-cell reinforcement learning (scRL), an actor–critic framework that recasts differentiation as a sequential decision process on an interpretable latent manifold derived with Latent Dirichlet Allocation. The critic learns state-value functions that quantify fate intensity for each cell, while the actor traces optimal developmental routes across the manifold. Benchmarks on hematopoiesis, mouse endocrinogenesis, acute myeloid leukemia, and gene-knockout and irradiation datasets show that scRL surpasses fifteen state-of-the-art methods in five independent evaluation dimensions, recovering early decision states that precede overt lineage commitment and revealing regulators such as Dapp1. Beyond fate decisions, the same framework produces competitive measures of lineage-contribution intensity without requiring ground-truth probabilities, providing a unified and extensible approach for decoding developmental logic from single-cell data. Full article
(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))
19 pages, 3993 KiB  
Article
Application of a ImageJ-Based Method to Measure Blood Flow in Adult Zebrafish and Its Applications for Toxicological and Pharmacological Assessments
by Ferry Saputra, Tzu-Ming Tseng, Franelyne P. Casuga, Yu-Heng Lai, Chih-Hsin Hung and Chung-Der Hsiao
Biology 2025, 14(1), 51; https://doi.org/10.3390/biology14010051 - 10 Jan 2025
Cited by 1 | Viewed by 1860
Abstract
Blood flow is an important physiological endpoint to measure cardiovascular performance in animals. Because of their innate transparent bodies, zebrafish is an excellent animal model for assessing in vivo cardiovascular performance. Previously, various helpful methods for measuring blood flow in zebrafish larvae were [...] Read more.
Blood flow is an important physiological endpoint to measure cardiovascular performance in animals. Because of their innate transparent bodies, zebrafish is an excellent animal model for assessing in vivo cardiovascular performance. Previously, various helpful methods for measuring blood flow in zebrafish larvae were discovered and developed. However, an optimized method to measure blood flow in adult zebrafish has not been reported. In this paper, the tail fin region was selected as target for blood flow measurements using the Trackmate method, provided by ImageJ platform. Based on power statistic calculations, the aortic vessel at the tail base was selected, and other parameters, such as ambient temperature, were investigated for method standardization, in order to minimize experimental variation. The method was also validated using fenpropathrin and ponatinib, which showed some cardiac alterations in a previous zebrafish study. We also checked the versatility of this method by following the same setup in black tetra and medaka and found that this method performed well. However, our results show that heavy pigmentation, like that found in tiger barb, and overlapping vessels, like those in parrot fish, make it hard for this method to perform well. Overall, an optimized protocol was used for the first time to measure blood flow velocity in adult wild-type zebrafish without the aid of transgenic lines or fluorescent dye. Full article
(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))
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11 pages, 1402 KiB  
Brief Report
A Deep Learning Approach to Measure Visual Function in Zebrafish
by Manjiri Patil, Annabel Birchall, Hammad Syed, Vanessa Rodwell, Ha-Jun Yoon, William H. J. Norton and Mervyn G. Thomas
Biology 2025, 14(6), 663; https://doi.org/10.3390/biology14060663 - 9 Jun 2025
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Abstract
Visual behaviour in zebrafish, often measured by the optokinetic reflex (OKR), serves as a valuable model for studying aspects of human neurological and ocular diseases and for conducting therapeutic or toxicology assays. Traditional methods for OKR analysis often rely on binarization techniques (threshold-based [...] Read more.
Visual behaviour in zebrafish, often measured by the optokinetic reflex (OKR), serves as a valuable model for studying aspects of human neurological and ocular diseases and for conducting therapeutic or toxicology assays. Traditional methods for OKR analysis often rely on binarization techniques (threshold-based conversion of images to black and white) or costly software, which limits their utility in low-contrast settings or hypopigmented disease models. Here, we present a novel deep learning pipeline for OKR analysis, using ResNet-50 within the DeepLabCut framework in a Python Version 3.10 environment. Our approach employs object tracking to enable robust eye movement quantification, regardless of variations in contrast or pigmentation. OKR responses were elicited in both wild-type and slc45a2 (albino) mutant zebrafish larvae at 5 days post-fertilisation, using a mini-LED arena with a rotating visual stimulus. Eye movements were recorded and analysed using both conventional software and our deep learning approach. We demonstrate that the deep learning model achieves comparable accuracy to traditional methods, with the added benefits of applicability in diverse lighting conditions and in hypopigmented larvae. Statistical analyses, including Bland–Altman tests, confirmed the reliability of the deep learning model. While this study focuses on 5-day-old zebrafish larvae under controlled conditions, the pipeline is adaptable across developmental stages, pigmentation types, and behavioural assays. With appropriate adjustments to experimental parameters, it could be applied to broader behavioural studies, including social interactions and predator–prey dynamics in ocular and neurological disease models. Full article
(This article belongs to the Special Issue AI Deep Learning Approach to Study Biological Questions (2nd Edition))
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