• The University of Western Australia (M519), 35 Stirling Highway,

    6009 Perth

    Australia

Calculated based on number of publications stored in Pure and citations from Scopus

Personal profile

Biography

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.

Current projects

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.

Research expertise keywords

  • Epigenetics
  • Image analysis
  • Machine learning / deep learning
  • Topological data analysis

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