Structure-based analysis of chemical reaction networks 1/2

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

Abstract: Inside living cells, chemical reactions form a large web of networks. Understanding the behavior of those complex reaction networks is an important and challenging problem. In many situations, it is hard to identify the details of the reactions, such as the reaction kinetics and parameter values. It would be good if we can clarify

Structure-based analysis of chemical reaction networks 2/2

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

Inside living cells, chemical reactions form a large web of networks. Understanding the behavior of those complex reaction networks is an important and challenging problem. In many situations, it is hard to identify the details of the reactions, such as the reaction kinetics and parameter values. It would be good if we can clarify what

다중 오믹스 분야의 현황 및 유전자-환경 상호 모델링의 필요성 (Current status of multi-omics research field and necessity of gene-by-environment (GxE) interaction modeling)

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

본 발표에서는 다양한 기초 생명-의학 분야에서 생성되고 있는 오믹스 자료의 연구 개발 현황에 대해서 다룰 예정이다. 보다 큰 규모로, 보다 빠르게, 보다 정확하게, 보다 정밀하게 라는 궁극적인 목표하에 이뤄지고 있는 오믹스 자료의 진화에 발맞춰, 이를 분석하는 수리통계적 모형 역시 진화하고 있다. 그 중, 이번 발표에서는 미국의 초 대형 정밀의료 프로젝트인 TopMed에서 진행하고 있는 COPD에 관한

Introduction to Bayesian Variable Selection.  

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

Abstract: Variable selection is an approach to identifying a set of covariates that are truly important to explain the feature of a response variable. It is closely connected or belongs to model selection approaches. This talk provides a gentle introduction to Bayesian variable selection methods. The basic notion of variable selection is introduced, followed by several Bayesian approaches with a simple application example.

Dynamical and topological hallmarks of regulatory networks driving phenotypic plasticity and heterogeneity in cancers

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) Abstract: Metastasis and therapy resistance cause over 90% of cancer-related deaths. Despite extensive ongoing efforts, no unique genetic or mutational signature has emerged for metastasis. Instead, the ability of genetically identical cells to adapt reversibly by exhibiting multiple phenotypes (phenotypic/non-genetic heterogeneity) and

Introduction to matrix and tensor factorization models and related stochastic nonconvex and constrained optimization algorithms

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

Abstract. Matrix/tensor factorization models such as principal component analysis , nonnegative matrix factorization, and CANDECOM/PARAFAC tensor decomposition provide powerful framework for dimension reduction and interpretable feature extraction, which are important in analyzing high-dimensional data that comes in large volume. Their diverse applications include image denoising and reconstruction, dictionary learning, topic modeling, and network data analysis.

Phase Estimation of Nonlinear State-space Model of the Circadian Pacemaker Using Level Set Kalman Filter and Raw Wearable Data

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

Abstract: Circadian rhythm is a robust internal 24 hours timekeeping mechanism maintained by the master circadian pacemaker Suprachiasmatic Nuclei (SCN). Numerous mathematical models have been proposed to capture SCN’s timekeeping mechanism and predict the circadian phase. There has been an increased demand for applying these models to the various unexplored data sets. One potential application

Dynamical System Perspective for Machine Learning

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

Abstract: The connection between deep neural networks and ordinary differential equations (ODEs) is an active field of research in machine learning. In this talk, we view the hidden states of a neural network as a continuous object governed by a dynamical system. The underlying vector field is written using a dictionary representation motivated by the

Optimized persistent random walk in zebrafish airineme search process

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

In addition to diffusive signals, cells in tissue also communicate via long, thin cellular protrusions, such as airinemes in zebrafish. Before establishing communication, cellular protrusions must find their target cell. In this talk, we demonstrate that the shapes of airinemes in zebrafish are consistent with a persistent random walk model. The probability of contacting the

Deep Learning-based Uncertainty Quantification for Mathematical Models

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon

Over the recent years, various methods based on deep neural networks have been developed and utilized in a wide range of scientific fields. Deep neural networks are highly suitable for analyzing time series or spatial data with complicated dependence structures, making them particularly useful for environmental sciences and biosciences where such type of simulation model

IBS 의생명수학그룹 Biomedical Mathematics Group
기초과학연구원 수리및계산과학연구단 의생명수학그룹
대전 유성구 엑스포로 55 (우) 34126
IBS Biomedical Mathematics Group (BIMAG)
Institute for Basic Science (IBS)
55 Expo-ro Yuseong-gu Daejeon 34126 South Korea
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