기초과학연구원 의생명수학그룹
We will discuss about "Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning." bioRxiv (2023): 2023-09. Abstract The recently proposed Chemical Reaction Neural Network (CRNN) discovers …
Abstract: Surface Plasmon Resonance (SPR) assays are a standard approach for quantifying kinetic parameters in antibody-antigen binding reactions. Classical SPR approaches ignore the bivalent structure of antibodies, and use simplified …
Abstract: I will report on some recent results on modelling the heart, the external circulation, and their application to problems of clinical relevance. I will show that a proper integration …
We will discuss about "An accurate probabilistic step finder for time-series analysis." bioRxiv (2023): 2023-09. Abstract Noisy time-series data is commonly collected from sources including Förster Resonance Energy Transfer experiments, …
We will discuss about "Hard limits and performance tradeoffs in a class of antithetic integral feedback networks." Cell systems 9.1 (2019): 49-63. Abstract Feedback regulation is pervasive in biology at …
Abstract: TBD
We will discuss about “Time delays modulate the stability of complex ecosystems” Nature Ecology & Evolution 7.10 (2023): 1610-1619. Abstract What drives the stability, or instability, of complex ecosystems? This question …
We will discuss about “Pulsed stimuli entrain p53 to synchronize single cells and modulate cell-fate determination” bioRxiv (2023): 2023-10. Abstract Entrainment to an external stimulus enables a synchronized oscillatory response across …
We will discuss about “MultiVI: deep generative model for the integration of multimodal data” Nature Methods 20.8 (2023): 1222-1231. Abstract Jointly profiling the transcriptome, chromatin accessibility and other molecular properties …
We will discuss about “Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery” IEEE Transactions on neural networks and learning systems 32.9 (2020): 4166-4177. Abstract Symbolic regression …
Abstract: Physiological needs evoke motivational drives to produce natural behaviours for survival. However, the temporally intertwined dynamics of need and motivation have made it challenging to differentiate these two components …
We will discuss about "AI Feynman: A physics-inspired method for symbolic regression",Science Advances 6.16 (2020): eaay2631. Abstract A core challenge for both physics and artificial intelligence (AI) is symbolic regression: …
