기초과학연구원 의생명수학그룹
In this talk, we discuss the paper "Achieving Occam’s razor: Deep learning for optimal model reduction" by Botond B. Antal et.al., PLOS Computational Biology, 2024. Abstract All fields of science depend on mathematical models. Occam’s razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This …
Abstract: The study of sleep and circadian rhythms at scale requires novel technologies and approaches that are valid, cost effective and do not pose much of a burden to the participant. Here we will present our recent studies in which we have evaluated several classes of technologies and approaches including wearables, nearables, blood based biomarkers …
In this talk we discuss the paper "Mathematical model for the distribution of DNA replication origins" by Alessandro de Moura and Jens Karschau, Physical Review E, 2024. Abstract DNAreplication in yeast and in many other organisms starts from well-defined locations on the DNA known as replication origins. The spatial distribution of these origins in the genome is particularly …
In this talk, we discuss the paper, “A frequency-amplitude coordinator and its optimal energy consumption for biological oscillators”, by Bo-Wei Qin et. al., Nature Communications, 2021. Zoom : https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 Abstract Biorhythm including neuron firing and protein-mRNA interaction are fundamental activities with diffusive effect. Their well-balanced spatiotemporal dynamics are beneficial for healthy sustainability. Therefore, calibrating both anomalous …
Abstract: There has been an increased use of scoring systems in clinical settings for the purpose of assessing risks in a convenient manner that provides important evidence for decision making. Machine learning-based methods may be useful for identifying important predictors and building models; however, their 'black box' nature limits their interpretability as well as clinical …
In this talk, we discuss the paper : "Noise robustness and metabolic load determine the principles of central dogma regulation" by Teresa W. Lo et al, Sci. Adv, https://doi.org/10.1126/sciadv.ado3095. Zoom: https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 Abstract The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light …
Abstravt: Latent space dynamics identification (LaSDI) is an interpretable data-driven framework that follows three distinct steps, i.e., compression, dynamics identification, and prediction. Compression allows high-dimensional data to be reduced so that they can be easily fit into an interpretable model. Dynamics identification lets you derive the interpretable model, usually some form of parameterized differential equations …
In this talk, we discuss the paper : "Derivation and simulation of a computational model of active cell populations: How overlap avoidance, deformability, cell-cell junctions and cytoskeletal forces affect alignment" by Leech et al, nature biotechnology, https://doi.org/10.1371/journal.pcbi.1011879. Zoom: https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 Abstract Collective alignment of cell populations is a commonly observed phenomena in biology. An important example …
Abstract: We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM) …
Abstract: The effect of malaria on the developing world is devastating. Each year there are more than 200 million cases and over 400,000 deaths, with children under the age of five the most vulnerable. Ambitious malaria elimination targets have been set by the World Health Organization for 2030. These involve the elimination of the disease …
In this talk, we discuss the paper "Next generation reservoir computing", by Gauthier, et.al, Nat. Comm., 2021. Abstract : Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly, it requires very small training data sets, uses linear optimization, and thus requires minimal computing resources. …
In this talk, we discuss the paper "SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection" by Yueyue Yao, et.al., Neural Networks, 2024. Abstract Anomaly detection in multivariate time series is of critical importance in many real-world applications, such as system maintenance and Internet monitoring. In this article, we propose a novel …
