In the field of biology, due to the advancement of DNA sequencing and
other high-throughput technologies, a vast amount of information has
been accumulated, from microscopic molecular and cellular information to
macroscopic individual characteristics. In order to exploit the wealth
of information from the huge data space, it is essential to
appropriately handle and analyze data with the knowledge of not only
biology (of the targets) but also information science and statistics. In
our laboratory, we aim to uncover the unknown relationships (missing
links) between genes and biological functions through meta-integrative
analysis techniques that comprehensively compare data obtained by
different labs in a variety of conditions for different purposes.
Single-cell epigenome analysis
- Kawaguchi RK, Tang Z, Fischer S, Rajesh C,
Tripathy R, Koo PK, Gillis J. “Learning
single-cell chromatin accessibility profiles using meta-analytic marker
genes.”, Briefings in Bioinformatics, bbac541, 2022.
- Sheu YJ, Kawaguchi RK, Gillis J, Stillman B.
“Prevalent and
dynamic binding of the cell cycle checkpoint kinase Rad53 to gene
promoters.”, eLife, 11:e84320, 2022.
Machine learning for small data in medical research
- Kawaguchi RK†, Takahashi M*,†, et al. “Assessing Versatile Machine Learning
Models for Glioma Radiogenomic Studies across Hospitals”, Cancers
(Basel), 13(14), 3611, 2021.
- Takahashi S, et al. “Fine-Tuning
Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images
with a Machine Learning Method to Normalize Image Differences among
Facilities”, Cancers (Basel), 13(6):1415, 2021.