Lin Liu

This paper introduces SSGATE, a dual-path graph attention auto-encoder for integrating single-cell and spatial multi-omics data, showing improved performance and robustness over existing methods.

spatiAlign integrates spatially resolved transcriptomics datasets using contrastive learning, improving batch effect removal and enabling downstream analysis across multiple tissue sections.

Spatial-ID improves cell type annotation in spatial transcriptomics by combining transfer learning and spatial embedding, outperforming existing methods on public datasets.

This paper introduces r-deFBA, a framework that integrates metabolic dynamics, resource allocation, and transcriptional regulation for predicting cellular behavior over time.

The paper introduces a metabolic-regulatory network model formalized as a hybrid automaton to integrate metabolism with transcriptional regulation and enzyme dynamics.