We will discuss about “Estimating and Assessing Differential Equation Models with Time-Course Data”, Wong, Samuel WK, Shihao Yang, and S. C. Kou, arXiv preprint arXiv:2212.10653 (2022). Abstract Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course …

We will discuss about “Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity”, Xia, Cedric Huchuan, et al., Neuropsychopharmacology 47.9 (2022): 1662-1671. Abstract Mapping individual differences in behavior is fundamental to personalized neuroscience, but quantifying complex behavior in real world settings remains a challenge. While mobility patterns captured by smartphones …

We will discuss about “Universal Differential Equations for Scientific Machine Learning”, Rackauckas, Christopher, et al.,arXiv preprint arXiv:2001.04385 (2020). Abstract In the context of science, the well-known adage "a picture is worth a thousand words" might well be "a model is worth a thousand datasets." In this manuscript we introduce the SciML software ecosystem as a …

We will discuss about “Dynamical information enables inference of gene regulation at single-cell scale”, Zhang, Stephen Y., and Michael PH Stumpf., bioRxiv (2023): 2023-01. Abstract Cellular dynamics and emerging biological function are governed by patterns of gene expression arising from networks of interacting genes. Inferring these interactions from data is a notoriously difficult inverse problem …