Yu Li

发表

conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics

Yongshuo Zong, Tingyang Yu, Xuesong Wang, 2022, bioRxiv.

fastMSA: Accelerating Multiple Sequence Alignment with Dense Retrieval on Protein Language

S. Sun, Qingxiong Tan, Liangzhen Zheng, 2021, bioRxiv.

Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions

Qingxiong Tan, Yixuan Wang, Zhihang Hu, 2022, bioRxiv.

E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction

Tao Shen, S. Sun, Liangzhen Zheng, 2022, ArXiv.

A scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data

Yu Li, Xingyu Fan, Yuqi Cheng, 2022, bioRxiv.

AcrNET: predicting anti-CRISPR with deep learning

Qingxiong Tan, Yixuan Wang, Yu Li, 2022, bioRxiv.

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis

Irwin King, Zhihang Hu, Yu Li, 2021, bioRxiv.

scMinerva: a GCN-featured Interpretable Framework for Single-cell Multi-omics Integration with Random Walk on Heterogeneous Graph

Yu Li, Yongshuo Zong, Tingyang Yu, 2022 .

DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment

Ramzan Umarov, Erik Arner, E. Arner, 2020, bioRxiv.

Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration

Jianzhu Ma, Zhihang Hu, Yu Li, 2021, bioRxiv.

CLMB: deep contrastive learning for robust metagenomic binning

Yixuan Wang, Yu Li, Pengfei Zhang, 2021, bioRxiv.

USPNet: unbiased organism-agnostic signal peptide predictor with deep protein language model

Qingxiong Tan, Yu Li, Shen-Xue Chen, 2021 .

Protein–RNA interaction prediction with deep learning: structure matters

Yu Li, Junkang Wei, Siyuan Chen, 2021, Briefings Bioinform..

Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

Yingying Wei, T. Chan, Yixuan Wang, 2021, bioRxiv.

Deep autoencoder enables interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

T. Chan, Yu Li, Yixuan Wang, 2021 .

HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides

Xingyu Fan, Qinze Yu, Zhihang Dong, 2021, bioRxiv.

Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation

Le Zhang, S. Sun, Yu Li, 2023, ArXiv.

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis

Irwin King, Yu Li, Siyuan Chen, 2022, Briefings Bioinform..

scMinerva: an Unsupervised Graph Learning Framework with Label-efficient Fine-tuning for Single-cell Multi-omics Integrated Analysis

Yongshuo Zong, Tingyang Yu, Xuesong Wang, 2023, bioRxiv.

DES-Mutation: System for Exploring Links of Mutations and Diseases

V. Bajic, A. Radovanovic, Vasiliki Kordopati, 2018, Scientific Reports.

Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model

Qingxiong Tan, Yu Li, Shen-Xue Chen, 2023, Nat. Comput. Sci..

Con-AAE: contrastive cycle adversarial autoencoders for single-cell multi-omics alignment and integration

Tingyang Yu, Xuesong Wang, Yixuan Wang, 2023, Bioinform..

SSBlazer: a genome-wide nucleotide-resolution model for predicting single-strand break sites

Yu Li, Junkang Wei, Sheng Xu, 2024, Genome biology.

Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

Yingying Wei, T. Chan, Yixuan Wang, 2021, bioRxiv.

GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling

Yu Li, Jun Ding, Adrien Osakwe, 2024, bioRxiv.