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Briefings in Bioinformatics|April 6, 2025
scMUG: deep clustering analysis of single-cell RNA-seq data on multiple gene functional modulesDe-Min Liang, Pu-Feng DuBriefings in Bioinformatics|March 15, 2023
iEssLnc: quantitative estimation of lncRNA gene essentialities with meta-path-guided random walks on the lncRNA-protein interaction networkYing-Ying Zhang, De-Min Liang, Pu-Feng DuCurrent Genomics|October 31, 2017
Predicting Protein Submitochondrial Locations: The 10th AnniversaryPu-Feng DuJournal of Theoretical Biology|December 26, 2015
Predicting Golgi-resident protein types using pseudo amino acid compositions: Approaches with positional specific physicochemical propertiesYa-Sen Jiao, Pu-Feng DuFrontiers in Genetics|March 4, 2025
WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seqYi-Ran Wang, Pu-Feng DuBriefings in Bioinformatics|January 3, 2024
SilenceREIN: seeking silencers on anchors of chromatin loops by deep graph neural networksJian-Hua Pan, Pu-Feng DuJournal of Theoretical Biology|May 8, 2016
Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: Approaches with minimal redundancy maximal relevance feature selectionYa-Sen Jiao, Pu-Feng DuJournal of Theoretical Biology|January 13, 2017
Predicting protein submitochondrial locations by incorporating the positional-specific physicochemical properties into Chou's general pseudo-amino acid compositionsYa-Sen Jiao, Pu-Feng DuFrontiers in Genetics|May 20, 2022
i5hmCVec: Identifying 5-Hydroxymethylcytosine Sites of <i>Drosophila</i> RNA Using Sequence Feature EmbeddingsHang-Yu Liu, Pu-Feng DuIEEE Journal of Biomedical and Health Informatics|October 26, 2021
NPI-RGCNAE: Fast Predicting ncRNA-Protein Interactions Using the Relational Graph Convolutional Network Auto-EncoderHan Yu, Zi-Ang Shen, Pu-Feng DuPageof 4