Mogens Jensen, Droplet formation, DNA repair and chaos in CellsBD
ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)Abstract: TBD
12 events found.
Seminar
Hyukpyo Hong, Inference and uncertainty quantification of stochastic gene expression via synthetic models
B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofWe 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 …
Dongju Lim, A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease
B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofWe 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 …
Pan Li, Modeling the circadian control of cardiac function
IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofAbstract: TBA
Thomas Philipp, Stochastic gene expression in lineage trees
ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)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 …
Nonparametric predictive model for sparse and irregular longitudinal data
B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofWe 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 …
Hyeontae Jo,Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning
B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofWe 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 …
Trivial but not trivial things in data science: From a statistical perspective
B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofTBA
Dongju Lim, Eui Min Jeong, Hyeontae Jo
Tea Room, IBS Daejeon, Daejeon, Korea, Republic ofDongju 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 …
Eui Min Jung, Uncovering specific mechanisms across cell types in dynamical models
B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofWe 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 …
Seonjin Kim, Nonparametric vs Parametric Regression
B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofTo 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. …
Sushmita Roy, Deciphering gene regulatory networks underlying cell-fate specification
ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)Abstract: Cell fate specification is a dynamic process during which gene regulatory networks (GRNs) transition between different states and define cell type-specific patterns of gene expression. Identifying such cell type-specific …