A Novel Approach to Predicting MiRNA-Disease Associations. - Guo Mao; Shu-Lin Wang (ICIC (2), 2019)

Prediction of Potential Associations Between MicroRNA and Disease Based on Bayesian Probabilistic Matrix Factorization Model. - Guo Mao; Shu-Lin Wang; Wei Zhang 089 (J. Comput. Biol., 2019)

A neuro-genetic approach for inferring gene regulatory networks from gene expression data. - Guo Mao; Zhengbin Pang; Jie Liu 002; Ke Zuo (ICBRA, 2022)

Reconstructing gene regulatory networks of biological function using differential equations of multilayer perceptrons. - Guo Mao; Ruigeng Zeng; Jintao Peng; Ke Zuo; Zhengbin Pang; Jie Liu 002 (BMC Bioinform., 2022)

An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data. - Guo Mao; Jie Liu (BIBM, 2023)

GGN: a model-free, data-driven deep learning framework for reconstructing gene regulatory networks. - Guo Mao; Ke Zuo; Zhengbin Pang; Jie Liu (ICBBB, 2023)

Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data. - Guo Mao; Zhengbin Pang; Ke Zuo; Jie Liu 002 (J. Comput. Biol., 2023)

Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks. - Guo Mao; Zhengbin Pang; Ke Zuo; Qinglin Wang; Xiangdong Pei; Xinhai Chen; Jie Liu 002 (Briefings Bioinform., 2023)

FMCC-RT: a scalable and fine-grained all-reduce algorithm for large-scale SMP clusters. - Jintao Peng; Jie Liu 002; Jianbin Fang; Min Xie; Yi Dai; Zhiquan Lai; Bo Yang 023; Chunye Gong; Xinjun Mao; Guo Mao; Jie Ren 007 (Sci. China Inf. Sci., 2025)