World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
70
Citations
19975
World Ranking
1873
National Ranking
256

Biology and Biochemistry

D-Index
71
Citations
21507
World Ranking
6561
National Ranking
207

Overview

Hong-Bin Shen is affiliated with Shanghai Jiao Tong University in China and has contributed extensively to the field of Biochemistry, Genetics and Molecular Biology, with a particular emphasis on Molecular Biology.

The research focus includes subfields such as Molecular Biology, Computational Theory and Mathematics, Artificial Intelligence, Cancer Research, and Computer Vision and Pattern Recognition.

Key topics covered in the research output include:

  • RNA and protein synthesis mechanisms
  • Protein Structure and Dynamics
  • Machine Learning in Bioinformatics
  • Computational Drug Discovery Methods
  • RNA Research and Splicing
  • Genomics and Phylogenetic Studies
  • Bioinformatics and Genomic Networks

Frequent co-authors collaborating with Hong-Bin Shen are:

  • Xiaoyong Pan
  • Chunqiu Xia
  • Ying Xia
  • Ye Yuan
  • Yi Fang

Publications are regularly found in several venues, including:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Bioinformatics
  • Briefings in Bioinformatics
  • arXiv (Cornell University)
  • Journal of Chemical Information and Modeling

Notable recent papers authored or co-authored by Hong-Bin Shen include:

  • Cell clustering for spatial transcriptomics data with graph neural networks, 2022, Nature Computational Science
  • GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues, 2021, Nucleic Acids Research
  • RBPsuite: RNA-protein binding sites prediction suite based on deep learning, 2020, BMC Genomics
  • lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning, 2021, Bioinformatics
  • ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity, 2020, Bioinformatics

Best Publications

  • Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms.

    Kuo-Chen Chou;Hong-Bin Shen

  • Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization

    Kuo-Chen Chou;Hong-Bin Shen

  • Recent progress in protein subcellular location prediction

    Kuo-Chen Chou;Hong-Bin Shen

  • PseAAC : A flexible web server for generating various kinds of protein pseudo amino acid composition

    Hong-Bin Shen;Kuo-Chen Chou

  • REVIEW : Recent advances in developing web-servers for predicting protein attributes

    Kuo-Chen Chou;Hong-Bin Shen

  • MemType-2L : A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM

    Kuo-Chen Chou;Hong-Bin Shen

  • A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0.

    Kuo-Chen Chou;Hong-Bin Shen

  • Ensemble classifier for protein fold pattern recognition

    Hong-Bin Shen;Kuo-Chen Chou

  • Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites.

    Kuo-Chen Chou;Hong-Bin Shen

  • Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms

    Kuo-Chen Chou;Hong-Bin Shen

  • The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier.

    Zhen Cao;Xiaoyong Pan;Yang Yang;Yan Huang

  • Signal-CF: A subsite-coupled and window-fusing approach for predicting signal peptides

    Kuo-Chen Chou;Hong-Bin Shen;Hong-Bin Shen

  • Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers.

    Kuo-Chen Chou;Hong-Bin Shen

  • EzyPred: A top–down approach for predicting enzyme functional classes and subclasses

    Hong-Bin Shen;Kuo-Chen Chou

  • Signal-3L: A 3-layer approach for predicting signal peptides.

    Hong-Bin Shen;Kuo-Chen Chou

  • Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization.

    Kuo-Chen Chou;Hong-Bin Shen

  • Large‐scale plant protein subcellular location prediction

    Kuo-Chen Chou;Hong-Bin Shen

  • Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites.

    Hong-Bin Shen;Kuo-Chen Chou

  • Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.

    Xiaoyong Pan;Peter Rijnbeek;Junchi Yan;Hong-Bin Shen

  • Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types.

    Hongbin Shen;Kuo-Chen Chou

  • Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM.

    Hong-Bin Shen;Hong-Bin Shen;Kuo-Chen Chou

Frequent Co-Authors

Kuo-Chen Chou
Kuo-Chen Chou The Gordon Life Science Institute
Dong-Jun Yu
Dong-Jun Yu Nanjing University of Science and Technology
Jingyu Yang
Jingyu Yang Nanjing University of Science and Technology
Jiangning Song
Jiangning Song Monash University
Yang Zhang
Yang Zhang University of Michigan–Ann Arbor
Shitong Wang
Shitong Wang Jiangnan University
Tatsuya Akutsu
Tatsuya Akutsu Kyoto University
Xibei Yang
Xibei Yang Jiangsu University of Science and Technology
Geoffrey I. Webb
Geoffrey I. Webb Monash University
Junchi Yan
Junchi Yan Shanghai Jiao Tong University

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