A local average connectivity-based method for identifying essential proteins from the network level. - Min Li 007; Jianxin Wang 001; Xiang Chen 029; Huan Wang; Yi Pan 001 (Comput. Biol. Chem., 2011)

PBMarsNet: A Multivariate Adaptive Regression Splines Based Method to Reconstruct Gene Regulatory Networks. - Siyu Zhao; Ruiqing Zheng; Xiang Chen 029; Yaohang Li; Fang-Xiang Wu; Min Li 007 (ISBRA, 2018)

DoRC: Discovery of rare cells from ultra-large scRNA-seq data. - Xiang Chen 029; Fang-Xiang Wu; Jin Chen 004; Min Li 007 (BIBM, 2019)

BiXGBoost: a scalable, flexible boosting-based method for reconstructing gene regulatory networks. - Ruiqing Zheng; Min Li 007; Xiang Chen 029; Fang-Xiang Wu; Yi Pan 001; Jianxin Wang 001 (Bioinform., 2019)

miRTMC: A miRNA Target Prediction Method Based on Matrix Completion Algorithm. - Hui Jiang 008; Mengyun Yang; Xiang Chen 029; Min Li 007; Yaohang Li; Jianxin Wang 001 (IEEE J. Biomed. Health Informatics, 2020)

An Ensemble Method to Reconstruct Gene Regulatory Networks Based on Multivariate Adaptive Regression Splines. - Ruiqing Zheng; Min Li 007; Xiang Chen 029; Siyu Zhao; Fang-Xiang Wu; Yi Pan 001; Jianxin Wang 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder. - Li Peng; Yuan Tu; Li Huang; Yang Li; Xiangzheng Fu; Xiang Chen 029 (Briefings Bioinform., 2022)

RNMFLP: Predicting circRNA-disease associations based on robust nonnegative matrix factorization and label propagation. - Li Peng; Cheng Yang; Li Huang; Xiang Chen 029; Xiangzheng Fu; Wei Liu 150 (Briefings Bioinform., 2022)

A deep graph convolution network with attention for clustering scRNA-seq data. - Xiang Chen 029; Junnan Yu; Li Peng; Min Li 007 (BIBM, 2023)

ESR: Optimizing Gene Feature Selection for scRNA-seq Data. - Tao Huang; Xiang Chen 029; Li Peng (CSCloud/EdgeCom, 2023)

scIAMC:Single-Cell Imputation via adaptive matrix completion. - Shuai Zhang; Xiang Chen 029; Li Peng (CSCloud/EdgeCom, 2023)

spGCLF: a versatile deep graph contrastive learning framework for spatial transcriptomics analysis. - Xiang Chen 029; Junnan Yu; Min Li 007 (BIBM, 2024)