Xingyi Li

Structure-aware deep learning predicts influenza antigenicity and guides vaccine strain recommendation

This paper introduces Vir3D, a structure-aware deep learning model that predicts influenza antigenicity and improves vaccine strain selection by incorporating 3D structural information from amino acid sequences.

A variational sparse Gaussian-process method for detecting spatially variable genes and cellular interactions from spatial transcriptomics

VISGP identifies spatially variable genes and cellular interactions from spatial transcriptomics data, outperforming existing methods in detecting ligand-receptor pairs.

SpatialClaw: A Memory-Augmented Autonomous Ecosystem for Spatial Omics Analysis

SpatialClaw provides a memory-augmented system for spatial omics analysis, enabling end-to-end workflows through natural language interaction and outperforming standard models in benchmarking.

Cell Type-informed Characterization of Spatial Niches from Spatial Multimodal and Multi-omics Data

SpaNECT is a framework for characterizing cell niches from spatial multimodal and multi-omics data, outperforming existing methods in diverse biological contexts.

Pathway-informed Universal Domain Adaptation for Single-cell RNA-seq Data

This paper introduces scPathOT, a pathway-informed domain adaptation method for single-cell RNA-seq data, demonstrating its ability to accurately annotate cell types and uncover functional mechanisms across diverse biological contexts.

FluNexus: a versatile web platform for antigenic prediction and visualization of influenza A viruses

FluNexus provides a web platform for antigenic prediction and visualization of influenza A viruses, offering tools for data preprocessing, prediction, and visualization of antigenic evolution.

Efficient Identification of Short Tandem Repeats via Context-Aware Motif Discovery and Ultra-Fast Sequence Alignment

FastSTR efficiently identifies and characterizes tandem repeats across 13 species with high accuracy and speed, uncovering new genomic elements and disease-related variations.

A Multimodal Graph Learning Framework for Versatile Spatial Transcriptomics Analysis with SpatialModal

SpatialModal integrates gene expression and histological images in spatial transcriptomics to improve spatial analysis and domain identification.

Spatial geometry-aware deep learning for deciphering tissue structure from spatially resolved transcriptomics

SpatialGEO improves identification of spatial regions in spatial transcriptomics data and reveals biological insights in tumor microenvironments and mouse development.

Deep Learning-Based Classification of CRISPR Loci Using Repeat Sequences

This paper introduces a deep learning method to classify CRISPR-Cas systems using repeat sequences, improving classification accuracy for subtypes with limited data.