2024/07/25 by Sindhura Kommu, Kommu, Sindhura, Yizhi Wang +5
Biochemistry, Genetics and Molecular Biology · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Gene expression and cancer classification #Machine Learning (cs.LG) #Single-cell and spatial transcriptomics
paper · pdf · doi:10.48550/arxiv.2407.18181
openalex publication_date 2024/07/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is a complex challenge that requires capturing the intricate relationships between genes and their regulatory interactions. In this study, we tackle this challenge by leveraging the single-cell BERT-based pre-trained transformer model (scBERT), trained on extensive unlabeled scRNA-seq data, to augment structured biological knowledge from existing GRNs. We introduce a novel joint graph learning approach that combines the rich contextual representations learned by pre-trained single-cell language models with the structured knowledge encoded in GRNs using graph neural networks (GNNs). By integrating these two modalities, our approach effectively reasons over boththe gene expression level constraints provided by the scRNA-seq data and the structured biological knowledge inherent in GRNs. We evaluate our method on human cell benchmark datasets from the BEELINE study with cell type-specific ground truth networks. The results demonstrate superior performance over current state-of-the-art baselines, offering a deeper understanding of cellular regulatory mechanisms.