Adaptive total-variation joint learning model for analyzing single cell RNA seq data. - Dai-Jun Zhang; Jing-Xiu Zhao; Jin-Xing Liu 001; Ying-Lian Gao (BIBM, 2021)

Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations. - Feng Zhou 021; Meng-Meng Yin; Cui-Na Jiao; Zhen Cui; Jing-Xiu Zhao; Jin-Xing Liu 001 (BMC Bioinform., 2021)

Non-Negative Low-Rank Representation With Similarity Correction for Cell Type Identification in scRNA-Seq Data. - Jin-Xing Liu 001; Dai-Jun Zhang; Jing-Xiu Zhao; Chun-Hou Zheng 001; Ying-Lian Gao (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

A Method Based On Dual-Network Information Fusion to Predict MiRNA-Disease Associations. - Feng Zhou 021; Meng-Meng Yin; Jing-Xiu Zhao; Junliang Shang; Jin-Xing Liu 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

Predicting miRNA-Disease Associations Through Deep Autoencoder With Multiple Kernel Learning. - Feng Zhou 021; Meng-Meng Yin; Cui-Na Jiao; Jing-Xiu Zhao; Chun-Hou Zheng 001; Jin-Xing Liu 001 (IEEE Trans. Neural Networks Learn. Syst., 2023)

A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection. - Dai-Jun Zhang; Ying-Lian Gao; Jing-Xiu Zhao; Chun-Hou Zheng 001; Jin-Xing Liu 001 (IEEE Trans. Neural Networks Learn. Syst., 2024)