Introduction to immersed boundary method for biofluids
B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofAbstract: TBA
12 events found.
Biomedical Mathematics Seminar
Deciphering circadian clock cell network morphology within the biological master clock, the suprachiasmatic nucleus
B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofAbstract: The biological master clock, the suprachiasmatic nucleus (SCN) of a mouse, contains many (~20,000) clock cells heterogeneous, particularly with respect to their circadian period. Despite the inhomogeneity, within an intact SCN, they maintain a very high degree of circadian phase coherence, which is generally rendered visible as system-wide propagating phase waves. The phase coherence …
Statistical Inference with Neural Network Imputation for Item Nonresponse
B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofAbstract: We consider the problem of nonparametric imputation using neural network models. Neural network models can capture complex nonlinear trends and interaction effects, making it a powerful tool for predicting missing values under minimum assumptions on the missingness mechanism. Statistical inference with neural network imputation, including variance estimation, is challenging because the basis for function …
Analysis of sleep-wake cycles via machine learning and mathematical modeling
B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofAbstract: TBA
Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC
B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofAbstract: Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC In this study, we discuss model robustness. Model robustness is consistent performance over variations of parameters. We formulate a stochastic target-mediated drug (TMDD) model, one of the pharmacokinetic models, to capture bi-exponential drug decay in plasma. A stochastic process is used to account …
Scalable Modeling Approaches in Systems Immunology
B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofAbstract: Systems biology seeks to build quantitative predictive models of biological system behavior. Biological systems, such as the mammalian immune system, operate across multiple spatiotemporal scales with a myriad of molecular and cellular players. Thus, mechanistic, predictive models describing such systems need to address this multiscale nature. A general outstanding problem is to cope with …
Bayesian model calibration and sensitivity analysis for oscillating biochemical experiments
B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofAbstract: Most organisms exhibit various endogenous oscillating behaviors, which provides crucial information about how the internal biochemical processes are connected and regulated. Along with physical experiments, studying such periodicity of organisms often utilizes computer experiments relying on ordinary differential equations (ODE) because configuring the internal processes is difficult. Simultaneously utilizing both experiments, however, poses a …
COVID19 – Mathematical Modeling and Machine Learning
B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofAbstract This presentation include the following two topics. First of all, we consider a spread model of COVID-19 with time-dependent parameters via deep learning. We developed a SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional model to confirm its convergent nature. Next, We also developed a …
Inferring causality in biological oscillators
B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofAbstract TBA
The Graph convolutional Networks (GCN) with Persistent Homology and its application 1/4
B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of(1) GCN and its Application. We introduce the GCN by reviewing the monumental paper " Semi-Supervised Classification with the Graph Convolutional Networks", ICLR 2018 by Kipf and Welling. We are going to much detail the algorithm of message aggregation and passings and learning processes. Code ; https://github.com/tkipf/gcn (2) Graph Attention networks(GAT) and its Applications. Bengio …
The Graph convolutional Networks (GCN) with Persistent Homology and its application 2/4
B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofSimplicial Complexes, Persistent Homology and Persistent Diagrams. After a brief review on the persistent homology( Cohen-Steiner, Edelsbrunner, Harer,2010), we discuss constructive procedures persistent diagrams from the persistent homology. Code; 9 software packages generating persistent homology are introduced at " A roadmap for the computation of persistent homology", EPJ Data Science, a Springer Open Journal.
The Graph convolutional Networks (GCN) with Persistent Homology and its applications 3/4
B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic ofNeural Networks with the Persistent Diagrams and Graph Classification. We introduce the first paper connecting persistent diagrams to the Neural Networks by Carrier et al," A neural Network Layer for Persistent Diagrams and New Graph Topological Signatures, 2019, arXiv. We are going to analyse the End-to-End algorithm and learning processes and applications. Code; tensorflow at …