World's Best Scientists 2026 revealed!
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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
45
Citations
8001
World Ranking
447
National Ranking
152

Computer Science

D-Index
54
Citations
9785
World Ranking
4638
National Ranking
621

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Leyi Wei is affiliated with Shandong University in China and has a research focus spanning biochemistry, genetics, molecular biology, and computer science. Their scholarly contributions cover both fundamental and applied aspects of biological sequence analysis and computational methodologies.

The scientist's core fields of study include:

  • Biochemistry, Genetics and Molecular Biology
  • Computer Science

Within these domains, their subfields of investigation encompass:

  • Molecular Biology
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Materials Chemistry

Leyi Wei's work addresses a number of principal topics, notably:

  • Machine Learning in Bioinformatics
  • Computational Drug Discovery Methods
  • RNA and protein synthesis mechanisms
  • Vaccines and immunoinformatics approaches
  • Protein Structure and Dynamics
  • Machine Learning in Materials Science
  • Genomics and Phylogenetic Studies

Their recent publications illustrate the application of computational techniques to biological problems, and include:

  • ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning, 2022, Bioinformatics
  • iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations, 2022, Genome Biology
  • DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis, 2023, Nucleic Acids Research
  • Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework, 2020, Briefings in Bioinformatics
  • ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism, 2021, Briefings in Bioinformatics

Publications by Leyi Wei frequently appear in several notable scientific journals and platforms, including:

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

The scientist collaborates regularly with a consistent group of peers. Frequent co-authors include:

  • Ran Su
  • Quan Zou
  • Junru Jin
  • Ruheng Wang
  • Feifei Cui

Best Publications

  • DUNet: A deformable network for retinal vessel segmentation

    Qiangguo Jin;Zhaopeng Meng;Zhaopeng Meng;Tuan D. Pham;Qi Chen

  • Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA.

    Quan Zou;Quan Zou;Pengwei Xing;Leyi Wei;Bin Liu

  • RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

    Qiangguo Jin;Zhaopeng Meng;Changming Sun;Leyi Wei

  • ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides.

    Leyi Wei;Chen Zhou;Huangrong Chen;Jiangning Song

  • Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information

    Leyi Wei;Jijun Tang;Jijun Tang;Quan Zou

  • mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation.

    Balachandran Manavalan;Shaherin Basith;Tae Hwan Shin;Leyi Wei

  • Improved and promising identification of human MicroRNAs by incorporating a high-quality negative set

    Leyi Wei;Minghong Liao;Yue Gao;Rongrong Ji

  • Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

    Leyi Wei;Pengwei Xing;Jiancang Zeng;JinXiu Chen

  • Prediction of human protein subcellular localization using deep learning

    Leyi Wei;Leyi Wei;Yijie Ding;Ran Su;Ran Su;Jijun Tang

  • Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response

    Ran Su;Xinyi Liu;Leyi Wei;Quan Zou

  • Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation

    Balachandran Manavalan;Shaherin Basith;Tae Hwan Shin;Leyi Wei

  • CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency

    Leyi Wei;PengWei Xing;Ran Su;Gaotao Shi

  • Fast Prediction of Protein Methylation Sites Using a Sequence-Based Feature Selection Technique

    Leyi Wei;Pengwei Xing;Gaotao Shi;Zhiliang Ji

  • PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning.

    Leyi Wei;Chen Zhou;Ran Su;Quan Zou

  • A novel hierarchical selective ensemble classifier with bioinformatics application

    Leyi Wei;Shixiang Wan;Jiasheng Guo;Kelvin Kl Wong

  • DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis

    Unknown

  • M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

    Leyi Wei;Huangrong Chen;Ran Su;Ran Su

  • iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations

    Unknown

  • Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species.

    Leyi Wei;Shasha Luan;Luis Augusto Eijy Nagai;Ran Su

  • Identifying enhancer–promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism

    Zengyan Hong;Xiangxiang Zeng;Leyi Wei;Xiangrong Liu

  • ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides.

    Bing Rao;Chen Zhou;Guoying Zhang;Ran Su

  • Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

    Ran Su;Jie Hu;Quan Zou;Balachandran Manavalan

  • Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites

    Leyi Wei;Leyi Wei;Ran Su;Ran Su;Bing Wang;Xiuting Li

Frequent Co-Authors

Ran Su
Ran Su Tianjin University
Quan Zou
Quan Zou University of Electronic Science and Technology of China
Jijun Tang
Jijun Tang University of South Carolina
Jiangning Song
Jiangning Song Monash University
Changming Sun
Changming Sun Commonwealth Scientific and Industrial Research Organisation
Wei Chen
Wei Chen Chengdu University of Traditional Chinese Medicine
André Leier
André Leier University of Alabama at Birmingham
Geoffrey I. Webb
Geoffrey I. Webb Monash University
Bin Liu
Bin Liu National University of Singapore
Jun Chen
Jun Chen Nankai University

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