Biomedical Mathematics Seminar

Abstract: 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 …

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Abstract: 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 …

Abstract: 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 …

Abstract 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 …

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(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 …

Simplicial 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.

Neural 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 …

Abstract Non-invasive data collection in real-world settings with wearables provides a new opportunity for characterizing daily physiology. However, accurate and efficient characterization remains an open problem because the complex autoregressive noise of the data makes it challenging to use a simple and efficient method for inference of clock proxies, least squares method. In this talk, …