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Yoshidome Lab
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The lab studies the hydration of proteins using theory and machine learning.
AboutAbout the lab
Reading protein hydration through theory and machine learning
The Yoshidome Lab (Mathematical Physics Division) studies the behavior of water around proteins (hydration) and related phenomena using liquid-state theory and machine learning.
Its research keywords include liquid-state theory, proteins, solvation and hydration, and manifold learning.
Electron microscopy and coherent X-ray diffraction imaging are also among its research keywords.
At the undergraduate level, the lab supervises students in the Applied Physics Course.
Key terms
- Hydration
- The gathering of water molecules around a dissolved substance, surrounding it
- Manifold learning
- A machine learning method that finds the low-dimensional structure, describable with few variables, underlying data represented by many variables
- Coherent X-ray diffraction imaging
- A method in which X-rays with aligned waves are directed at a sample and an image is obtained from the resulting diffraction pattern
NewsLab news
Announced a result predicting gas diffusion in fuel cells in about 10 seconds using nano-CT and machine learning
Announced a result making protein hydration analysis fast enough for practical use with deep learning
A student receives the Applied Physics Course Graduation Research Presentation Award
Associate Professor Yoshidome receives a Poster Award at the CBI Society 2024 Annual Meeting
A student receives the Applied Physics Course Graduation Research Presentation Award
ResearchResearch themes
The lab studies the water surrounding proteins (hydration) using liquid-state theory and deep learning, working to make the analysis fast enough for practical use. It is also involved in research that combines nano-CT images with machine learning to predict gas diffusion in fuel cell catalyst layers.
*Illustrative imageAnalyzing Protein Hydration with Deep Learning
The lab uses deep learning to analyze the hydration around proteins in less time than conventional methods. It aims to apply this to drug discovery.
*Illustrative imagePredicting Gas Diffusion in Fuel Cells with Machine Learning
Using nano-CT images from NanoTerasu and machine learning, the lab has achieved prediction of gas diffusion in fuel cell catalyst layers in about 10 seconds. Tohoku University and JST announced the result in a press release.
MembersFaculty
- Associate ProfessorTakashi Yoshidome
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “HydrationLayer2New” Babusingh252 at en.wikipedia / Public domain (cropped)
- Analyzing Protein Hydration with Deep Learning: “Water-3D-balls-A” Ben Mills / Public domain (cropped)
- Predicting Gas Diffusion in Fuel Cells with Machine Learning: “Hydrogen fuel cell schematic” Emma Ambrogi / CC BY-SA 4.0 (cropped)
- Related lab (Image Information and Communication Engineering [Omachi-Miyazaki] Lab): “Image processing post otsus algorithm” Pikez33 / CC BY 1.0 (cropped)
- Related lab (Tumor Biomedical Engineering [Kodama] Lab): “Scanning electron microscope - UFCH JH (2020) 04” Tadeáš Bednarz / CC BY-SA 4.0 (cropped)
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