기초과학연구원 의생명수학그룹
On Nov 1st, 2023, Eder Zavala from the University of Birmingham gave an online talk at the IBS Biomedical Mathematics Colloquium. The title of his talk was “Quantitative analysis of high-resolution daily profiles of HPA axis hormones.”
On October 27th, 2023, Hyun Kim gave a talk on “Significance analysis for clustering with single-cell RNA-sequencing data”
On Oct 20, 2023, Tetsuya J. Kobayashi from the University of Tokyo gave an online talk at the IBS Biomedical Mathematics Colloquium. The title of his talk was “Optimality of Biological Information Processing.”
On October 20th, 2023, Hyeontae Jo gave a talk on “AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records”
We are thrilled to announce the upcoming launch of our cutting-edge mobile application, SleepWake, tailored to optimize sleep patterns, along with our groundbreaking AI algorithm designed to detect sleep disorders. This noteworthy achievement has attracted the attention of JTBC News, a prestigious news outlet in Korea, which has showcased our AI algorithm and the SleepWake application.
On October 6th, 2023, Olive Cawiding gave a talk on “Power spectral estimate for discrete data”
On September 22, 2023, Yun Min Song gave a talk on “A data-driven approach for timescale decomposition of biochemical reaction networks”
On September 19th, 2023, Balazs Erdos from Maastricht University gave a talk at the IBS Biomedical Mathematics Seminar. The title of his talk was “Quantifying the dynamics of postmeal metabolism: Inference from challenge test data.”
On Sep 20, 2023, Sebastian Walcher from Mathematik A, RWTH Aachen gave an online talk at the IBS Biomedical Mathematics Colloquium. The title of his talk was “eaction networks: Reduction of dimension and critical parameters.”
He shared the talk slides. You can download the slides here.
On September 15, 2023, Eui Min Jung gave a talk on “Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks”