In this talk, we discuss the paper "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" by Haotian Cui, et.al. Nature Methods, 2024. Abstract Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged …

In this talk, we discuss the paper "Effective Markovian dynamics method of solving non-Markovian dynamics of stochastic gene expression" by Youming Li and Chen Jia, Physical Review Letters, to appear. Abstract Experiments have shown that over 10% of proteins are degraded non-exponentially. Gene expression models for non-exponentially degraded proteins are notoriously difficult to solve since the underlying …

In this talk, we discuss the paper "Quantifying the energy landscape of high-dimensional oscillatory systems by diffusion decomposition" by S. Bian et.al., Cell Reports Physical Science, 2025. Abstract High-dimensional networks producing oscillatory dynamics are ubiquitous in biological systems. Unraveling the mechanism of oscillatory dynamics in biological networks with stochastic perturbations becomes of paramount significance. Although …

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 …