Journal Club

We will discuss about “Data driven governing equations approximation using deep neural networks” Journal of Computational Physics (2019). Abstract We present a numerical framework for approximating unknown governing equations using observation data and deep neural networks (DNN). In particular, we propose to use residual network (ResNet) as the basic building block for equation approximation. We demonstrate that the ResNet block can be …

In this talk, we discuss the paper "Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe", by Xiaojie Qiu  et.al., Cell Syst. 2020. Abstract  Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe …

In this talk, we discuss the paper, "MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data" by Siyao Liu et.al.  Genome Biology, 2024. Abstract  Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) …

In this talk, we will discuss the paper, "A modified shuffled frog leaping algorithm with inertia weight", by Zhuanzhe Zhao et.al. , Scientific Reports, 2024. Abstract  The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, …