In this talk, we discuss the paper "Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan" by J. Shim et.al., npj digital medicine, 2024. Abstract Recognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables, we introduce CosinorAge, a digital biomarker of aging …

In this talk, we discuss the paper "Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters" by L. Xia et.al. Nature Communications, 2024. Abstract Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection …

In this talk, we discuss the paper "Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains" by K. Lee. Abstract The ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases, considering the inherent properties of datasets. In tabular domains, it is critical to effectively handle heterogeneous features …

In this talk, we discuss the paper "Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics" by J. Thompson et al., bioarxiv, 2025. Abstract Microbial communities play essential roles in shaping ecosystem functions and predictive modeling frameworks are crucial for understanding, controlling, and harnessing their properties. Competition and cross-feeding of metabolites …

In this talk, we discuss the paper "Data-driven model discovery and model selection for noisy biological systems" by Xiaojun Wu et al., PLOS Computational Biology, 2025. Abstract Biological systems exhibit complex dynamics that differential equations can often adeptly represent. Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of …

In this talk, we discuss the paper "SCassist: An AI Based Workflow Assistant for Single-Cell Analysis " by Vijayaraj Nagarajan et al., bioarxiv, 2025.  Abstract Single-cell RNA sequencing (scRNA-seq) data analysis often involves complex iterative workflow, requiring significant expertise and time. To navigate this complexity, we have developed SCassist, an R package that leverages the power …