Computational Biology Group/Core LaboratoriesKiryu Laboratory
(Laboratory of Biological Network Analysis)

Our laboratory is focusing on making biological discoveries through the application of statistical methods to genome-scale data such as genome sequences, microarray data, and next-generation sequencer data. We are also working on developing new probabilistic and mathematical tools that are necessary for such analysis. Since the first successes in the 1990s, researchers have succeeded in decoding the full genome of thousands of species. The information generated from those efforts is not limited to genome sequences, but also includes other building blocks of life such as RNA, proteins, metabolites, and DNA modifications. However, integrated analysis of such extremely heterogeneous data has only just begun,and many problems await solutions. We are applying statistical techniques to detect faint signals in the noise that will lead to a deeper understanding of life.

Computational Biology, Bioinformatics, Biological Networks, Probabilistic Models, Artificial Intelligence
Estimating Cell Differentiation Progression from Single-Cell Resolution Gene Expression Data

Advances in sequencing technology have enabled comprehensive measurement of how cells activate specific genes under various conditions. Particularly, recent advancements in single-cell RNA-seq techniques have made it possible to measure RNA activity profiles for each individual cell among tens of thousands. We have developed a method that models the internal state of cells using a probabilistic process called the Ornstein-Uhlenbeck process and optimizes its unknown parameters through machine learning. Subsequently, we applied this method to single-cell RNA-seq data from stimulated cells to develop a novel technique for estimating the differentiation progression of each cell.

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