activity
20232025
most citedOn the Identifiability and Interpretability of Gaussian Process Models

2 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

stat.AP2025

Large-scale spatial variable gene atlas for spatial transcriptomics

Jiawen Chen, Jinwei Zhang, Dongshen Peng +4

Spatial variable genes (SVGs) reveal critical information about tissue architecture, cellular interactions, and disease microenvironments. As spatial transcriptomics (ST) technolog…

stat.ME2025

The Nearest-Neighbor Derivative Process: Modeling Spatial Rates of Change in Massive Datasets

Jiawen Chen, Aritra Halder, Yun Li +2

Gaussian processes (GPs) are instrumental in modeling spatial processes, offering precise interpolation and prediction capabilities across fields such as environmental science and…

q-bio.GN2025★ 1 cited

CellTypeAgent: Trustworthy cell type annotation with Large Language Models

Jiawen Chen, Jianghao Zhang, Huaxiu Yao +1

Cell type annotation is a critical yet laborious step in single-cell RNA sequencing analysis. We present a trustworthy large language model (LLM)-agent, CellTypeAgent, which integr…

cs.CV2024

STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics

Jiawen Chen, Muqing Zhou, Wenrong Wu +3

Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields,…

stat.ME2024

Gaussian Processes for Time Series with Lead-Lag Effects with applications to biology data

Wancen Mu, Jiawen Chen, Eric S. Davis +4

Investigating the relationship, particularly the lead-lag effect, between time series is a common question across various disciplines, especially when uncovering biological process…

stat.ML2023★ 2 cited

On the Identifiability and Interpretability of Gaussian Process Models

Jiawen Chen, Wancen Mu, Yun Li +1

In this paper, we critically examine the prevalent practice of using additive mixtures of Matérn kernels in single-output Gaussian process (GP) models and explore the properties of…