Development of Data-Driven Mathematical Analysis for Single Cells and Singularity Cells

Name: Tamiki Komatsuzaki
Affiliation: Research Center of Mathematics for Social Creativity
Research Institute for Electronic Science
Hokkaido University
Major: Mathematical Science, Chemical Physics, Biophysics
Role: Development of techniques to predict singularity cells

Some singularity phenomena are known such as the emergence of leader-like and follower-like cells, that is, leader cells lead the neighboring cells and follower cells move on with the lead among cells, and the persistence toward antibiotics depending on the phenotypic differences such as the amount of intracellular protein. However, their mechanism is not yet certain. In our project, we develop an information analysis platform to answer the following questions; “Can we identify the singularity? By observing what? How to analyze?”, in other words, “How can we quantify, extract, and predict the singularity cells from time series data set of large amounts of features?”.

Name: Atsuyoshi Nakamura
Affiliation: Graduate School of Information Science and Technology, Hokkaido University
Major: Online Learning, Active Learning
Role: Development of techniques to search highly effectively for the rules of singularity cell classification

Name: Shunsuke Ono
Affiliation: School of Computing, Tokyo Institute of Technology
Major: Image processing, Signal processing
Role: Development of techniques of singularity cells image processing

Name: Ichigaku Takigawa
Affiliation: RIKEN Center for Advanced Intelligence Project
Major: Machine Learning
Role: Giving advices related to machine learning technology