Yutong Lu

CellFM is a large-scale foundation model pre-trained on 100 million human cells, outperforming existing models in cell annotation, perturbation prediction, and gene function prediction.

GraphBepi predicts B-cell epitopes using AlphaFold2 structures and a pretrained language model, outperforming existing methods in AUC and AUPR.

This paper introduces ConGI, a method that integrates gene expression and histopathological images via contrastive learning to improve spatial domain identification in spatial transcriptomics.

Hist2ST predicts RNA-seq expression from histology images using a deep learning model combining Transformer and graph neural networks, outperforming existing methods in gene expression prediction and spatial region identification.

ADClust is a parameter-free deep embedding clustering method for scRNA-seq data that automatically determines the number of clusters and outperforms existing methods.

This paper introduces GraphCS, a scalable GNN method for single-cell classification, showing improved performance on large datasets.

DGAT-onco detects oncogenes by integrating differential mutational analysis and functional impacts of somatic mutations, identifying 22.8% of its results as verified oncogenes.

This paper introduces GraphSCI, a method that imputes dropout events in scRNA-seq data using graph convolution networks, outperforming existing methods on simulated and real data.

The paper introduces a new basecalling method for Oxford Nanopore sequencing that improves accuracy and speed compared to existing tools.

GraphSCC improves single-cell clustering by incorporating structural relations between cells using graph convolutional networks and a dual self-supervised module.

This paper introduces TH-GRASP, an XGBoost-based method for predicting genome-wide RNA secondary structure profiles, achieving high AUC values and demonstrating effectiveness in reducing experimental noise.

The paper introduces MTDsite, a multi-task deep learning method for predicting protein-binding residues across DNA, RNA, peptide, and carbohydrate, achieving higher accuracy than existing methods.

This paper introduces SPROF, a method using 2D pairwise residue distance maps and image captioning to predict protein sequence profiles, achieving a 5.2% improvement over SPIN2.