김재경 교수

Abstract Competitive target-mediated drug disposition (competitive TMDD) arises when two drugs compete for the same target receptor. These dynamics can be characterized by the full competitive TMDD model; yet, its complexity motivated the use of a reduced model, which is invalid under high receptor concentrations. While this problem can be resolved by using the total quasi-steady state approximation (tQSSA), which remains valid for all receptor conditions, the exact formula of the tQSSA-based re

• Fully automated, data-driven clustering pipeline for scRNA-seq analysis. • Addresses the limitation of fixed principal component defaults in DR. • Data-driven pipeline improves clustering performance by 10-14%. • Performance gains are most pronounced on high-sparsity, high-skewness data. Single-cell RNA sequencing (scRNA-seq) provides deep insights into cellular heterogeneity but demands robust dimensionality reduction (DR) and clustering to handle high-dimensional, noisy data. Many DR and clu

Complex systems can often be analyzed at either the microscale of their individual components or the macroscale of their collective organization, yet it remains debated which level of description offers the most meaningful causal understanding. Hoel's recent study in Patterns addresses this challenge by introducing Causal Emergence 2.0, a novel formalization showing that a system's causal workings are best described by how causal influence is distributed across its hierarchy of scales.

Abstract Target-mediated drug disposition (TMDD) models have been widely used to describe nonlinear pharmacokinetic profiles driven by high-affinity, low-capacity drug–target binding. A pTMDD model, derived by applying the Padé approximation of the quasi-steady-state (QSS) model (qTMDD) was previously proposed. Although pTMDD model showed a comparable estimation accuracy while maintaining computational efficiency, further validation in realistic clinical scenarios and comprehensive performance e