| Professor | Shinpei Kawaoka |
| Assistant Professor | Mayuko Yoda |
Understanding Complex Biological Phenomena through Large-Scale Data Analysis
Our laboratory seeks to answer fundamental questions by leveraging large-scale data analysis: Why does cancer lead to systemic dysfunction? How are daily stress and aging interconnected? What biological effects does device usage have on the human body? Recent technological advances have dramatically expanded the amount of information that can be obtained in life sciences and medicine. Sequencing an entire genome is no longer difficult. It is now possible to comprehensively measure the expression levels of more than 20,000 genes within a single cell, as well as hundreds of metabolites. In addition, extensive background information on individuals—such as age, sex, and medical history—can now be accumulated and analyzed. While technological innovation has greatly increased data availability, it has also introduced new challenges. Even when large-scale data are obtained, interpreting what the information truly means is often difficult. Integrating data across different layers—for example, linking gene expression profiles with clinical histories—remains particularly challenging. Addressing these fundamental questions has become increasingly important: How can we generate data that are inherently interpretable? How can we extract meaningful insights from complex, large-scale datasets? Our laboratory aims to build an integrated framework that maximizes the value of large-scale data by conducting experimental design, high-throughput measurement, and data analysis in a seamless and unified manner.
Research Topics
・Cancer cachexia
・Enhancer-dependent regulation of metabolism, immunity, and aging
・Multi-omics changes during daily human activities
Selected Publications
1. Hojo et al. Nat Commun. 2019 Jun 13;10(1):2603. doi: 10.1038/s41467-019-10525-1.
2. Mizuno et al. Nat Commun. 2022 Jun 15;13(1):3346. doi: 10.1038/s41467-022-30926-z.
3. Vandenbon et al. Commun Biol. 2023 Jan 24;6(1):97. doi: 10.1038/s42003-023-04479-w.
4. Maeshima et al. eBioMedicine 2024 Sep:107:105271. doi: 10.1016/j.ebiom.2024.105271.
5. Nakamura et al. Cancer Sci. 2024 Mar;115(3):715-722. doi: 10.1111/cas.16078.
Research Interests
Cancer Cachexia, Multi-Omics Analysis, Enhancer, Human Biology, Large-scale data analysis