기초과학연구원 의생명수학그룹
We introduce a sufficient graphical model by applying the recently developed nonlinear sufficient dimension reduction techniques to the evaluation of conditional independence. The graphical model is nonparametric in nature, as …
Abstract: TBD
We will discuss about “Inference and uncertainty quantification of stochastic gene expression via synthetic models”, Öcal et al., J. R. Soc. Interface. Abstract Estimating uncertainty in model predictions is a …
We will discuss about “A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease”, Alexandersen, Christoffer G., et al., Journal of the Royal Society Interface 20.198 (2023): 20220607. Abstract …
Abstract: TBA
Abstract: Stochasticity in gene expression is an important source of cell-to-cell variability (or noise) in clonal cell populations. So far, this phenomenon has been studied using the Gillespie Algorithm, or …
We propose a kernel-based estimator to predict the mean response trajectory for sparse and irregularly measured longitudinal data. The kernel estimator is constructed by imposing weights based on the subject-wise …
We will discuss about “Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning”, Zhao, Shuai, et al., IEEE Transactions on Power Electronics 37.10 (2022): 11567-11578. Abstract Physics-informed machine learning …
TBA
Dongju Lim: Mood Prediction for Bipolar Disorder Patient with Sleep Pattern Information Eui Min Jeong:Noise attenuation through the multiple repression mechanism in transcription Hyeontae Jo: Parameter estimation with …
We will discuss about “Uncovering specific mechanisms across cell types in dynamical models”, Hauber, Adrian Lukas, Marcus Rosenblatt, and Jens Timmer., bioRxiv (2023): 2023-01. Abstract Ordinary differential equations are frequently employed …
To understand nonparametric regression, we should know first what the parametric model is. Simply speaking, the parametric regression model consists of many assumptions and the nonparametric regression model eases the assumptions. …
