AI Deep Learning Approach to Study Biological Questions

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 experienced 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, entitled “AI Deep Learning Approach to Study Biological Questions”, in Biology will particularly welcome researchers who use deep learning or machine vision to address diverse biological questions relating from fundamental to biomedical 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 to help wet-lab biological researchers ask 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, “AI deep learning approach to study biological questions”.

[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

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