Xiao Luo

RobustCell: A Model Attack-Defense Framework for Robust Transcriptomic Data Analysis

RobustCell evaluates attack-defense methods in single-cell and spatial transcriptomic data analysis, showing attacks can impair clustering and annotation, while defenses can protect model performance.

SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics

SPEAK identifies spatial domains in spatial transcriptomics data using a large language model and expert-aligned prompts, showing improved accuracy and interpretability.

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis

scELMo uses Large Language Models to analyze single-cell data for clustering, batch correction, and cell-type annotation without retraining, outperforming existing methods.

VISTA Uncovers Missing Gene Expression and Spatial-induced Information for Spatial Transcriptomic Data Analysis

VISTA improves spatial transcriptomic data by imputing missing gene expression and enables downstream analyses like ligand-receptor discovery and spatial velocity inference.

Building A Unified Model for Drug Synergy Analysis Powered by Large Language Models

This paper introduces BAITSAO, a unified model for drug synergy prediction using large language models, showing improved performance over existing methods.

RaptorX-Single: single-sequence protein structure prediction by integrating protein language models

RaptorX-Single predicts protein structures from single sequences using language models and a structure generation module, outperforming MSA-based methods in specific cases.

Deepprune: Learning efficient and interpretable convolutional networks through weight pruning for predicting DNA-protein binding

Deepprune prunes and fine-tunes CNNs to improve motif inference with fewer kernels, achieving better performance on simulated and ChIP-seq data.

Expectation pooling: An effective and interpretable pooling method for predicting DNA-protein binding

This paper introduces expectation pooling, an interpretable CNN pooling method that improves DNA-protein binding prediction by combining probabilistic ideas with deep learning.