Research Overview

In recent years, understanding biological systems at the cellular level through comprehensive omics data has become essential in the life sciences. However, the integration and analysis of omics datasets affected by batch effects and systematic variation require sophisticated mathematical and statistical approaches. Furthermore, artificial intelligence and machine learning technologies, which can efficiently explore vast parameter spaces, are expected to enable further breakthroughs in predicting stochastic cell fate decisions.

Our laboratory develops methodologies for large-scale comparative analyses of biological data by leveraging interdisciplinary approaches from computer science, statistics, mathematics, and machine learning. Through meta-analytic approaches that integrate large volumes of publicly available data, we construct gene co-expression networks to better understand gene expression regulation. Using a unified analytical framework, we seek to elucidate diverse biological phenomena, including single-cell heterogeneity, age-associated changes, and the processes through which organisms have acquired robustness and complexity over the course of evolution.

Our current research focuses on the construction of co-expression networks using RNA-seq data, the identification of cell-type-specific enhancers through single-cell RNA-seq and ATAC-seq analyses, RNA structure and post-transcriptional regulation, and the evolutionary dynamics of regulatory regions. By applying these approaches to comparative studies across diverse human cell types, including iPS cells, as well as across different species, we aim to elucidate universal principles underlying biological systems.