Profile
Kenta Ninomiya is a Post-doctoral Research Associate at the Chromatin and Ageing Laboratory at the Harry Perkins Institute of Medical Research, UWA Medical School at The University of Western Australia, working with Prof Alexey V. Terskikh. He specializes in biomedical and molecular biological image analysis, applying machine learning and computational topology to decipher complex epigenetic mechanisms.
Kenta received his PhD degree in Health Sciences from Kyushu University, Japan, in 2022, where his research focused on the relationship between medical imaging and gene expression. He then transitioned to Sanford Burnham Prebys Medical Discovery Institute, where he focused on single-cell epigenetics for ageing research, before moving to Perth to join the Perkins.
Kenta’s current research focuses on developing a novel approach to analyse and explain the intricate epigenetic mechanisms regulating gene expression. He is particularly interested in understanding the combinatorial effects and spatial organisation of histone modifications within single-cell nuclei. To achieve this, he employs a range of cutting-edge techniques, including:
- Advanced imaging: analysing high-resolution molecular images to capture the spatial/time distribution of histone modifications.
- Mathematical modelling: applying topology theory and hyperbolic geometry to model the intricate spatial relationships of histone modifications within the 3D nuclear and feature space.
- Machine learning: utilising diverse machine learning models (e.g. multi-layer perceptron, graph neural networks, hyperbolic neural networks, simplex neural networks, and transformers) to identify patterns and predict epigenetic interactions.
This innovative approach has the potential to revolutionise our understanding of dynamic epigenetics, drug mechanisms, and in-situ analysis, ultimately paving the way for the development of novel therapeutic strategies targeting epigenetic mechanisms in various diseases.
Selected Publications
Alvarez-Kuglen M, Ninomiya K, Qin H, Rodriguez D, Fiengo L, Farhy C, Hsu WM, Kirk B, Havas A, Feng GS, Roberts AJ, Anderson RM, Serrano M, Adams PD, Sharpee TO and Terskikh AV, Sept 2024, ImAge quantitates aging and rejuvenation. In: Nature Aging. 4, 9, p. 1308-1327 20 p.
Ninomiya K and Terskikh AV, Sept 2024, Imaging the epigenetic landscape in single cells to study aging trajectories. In: Nature Aging. 4, 9, p. 1184-1185 2 p.
Ninomiya K, Arimura H, Tanaka K, Chan WY, Kabata Y, Mizuno S, Gowdh NFM, Yaakup NA, Liam C-K, Chai C-S, et al. (2023). Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients. In: Comput. Methods Programs Biomed. 236, 107544.
Ninomiya K, Arimura H, Yoshitake T, Hirose T-A and Shioyama Y. (2022). Synergistic combination of a topologically invariant imaging signature and a biomarker for the accurate prediction of symptomatic radiation pneumonitis before stereotactic ablative radiotherapy for lung cancer: A retrospective analysis. In: PLOS One 17, e0263292.
Ninomiya K, Arimura H, Chan WY, Tanaka K, Mizuno S, Muhammad Gowdh NF, Yaakup NA, Liam C-K, Chai C-S, and Ng KH. (2021). Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers. In: PLoS One 16, e0244354.
Ninomiya K, and Arimura H. (2020). Homological radiomics analysis for prognostic prediction in lung cancer patients. In: Phys. Med. 69, 90–100.
Ninomiya K, Arimura H, Sasahara M, Kai Y, Hirose T, and Ohga S. (2018). Feasibility of anatomical feature points for the estimation of prostate locations in the Bayesian delineation frameworks for prostate cancer radiotherapy. In: Radiol. Phys. Technol. 11, 434–444.