De-Shuang Huang


Affiliation: Tongji University
Location: Shanghai, China


  1. Shen Z, Bao W, Huang D. Recurrent Neural Network for Predicting Transcription Factor Binding Sites. Sci Rep. 2018;8:15270 pubmed publisher
    ..The robustness of KEGRU is proved by experiments with different k-mer length, stride window and embedding vector dimension. ..
  2. request reprint
    Huang D, Zhang L, Han K, Deng S, Yang K, Zhang H. Prediction of protein-protein interactions based on protein-protein correlation using least squares regression. Curr Protein Pept Sci. 2014;15:553-60 pubmed
    ..Therefore, LSR(+) is a powerful tool to characterize the protein-protein correlations and to infer PPI, whilst keeping high performance on prediction of PPI networks. ..
  3. Yi H, You Z, Huang D, Li X, Jiang T, Li L. A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information. Mol Ther Nucleic Acids. 2018;11:337-344 pubmed publisher
  4. Chuai G, Ma H, Yan J, Chen M, Hong N, Xue D, et al. DeepCRISPR: optimized CRISPR guide RNA design by deep learning. Genome Biol. 2018;19:80 pubmed publisher
    ..In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at . ..
  5. Deng S, Huang D. SFAPS: an R package for structure/function analysis of protein sequences based on informational spectrum method. Methods. 2014;69:207-12 pubmed publisher
    ..It is released under the GNU General Public License. The R package along with its source code and additional material are freely available at ..
  6. Zhang H, Zhu L, Huang D. WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data. Sci Rep. 2017;7:3217 pubmed publisher
  7. Guo W, Huang D. An efficient method to transcription factor binding sites imputation via simultaneous completion of multiple matrices with positional consistency. Mol Biosyst. 2017;13:1827-1837 pubmed publisher
    ..We anticipate that our approach will constitute a useful complement to experimental mapping of TF binding, which is beneficial for further study of regulation mechanisms and disease. ..
  8. Bao W, Jiang Z, Huang D. Novel human microbe-disease association prediction using network consistency projection. BMC Bioinformatics. 2017;18:543 pubmed publisher
    ..It is anticipated that NCPHMDA would become an effective biological resource for clinical experimental guidance. ..