Selected Publications

# These authors contributed equally to the paper as first authors

* To whom correspondence should be addressed


2022


  • [32] Predicting protein-peptide binding residues via interpretable deep learning

    Ruheng Wang, Junru Jin, Quan Zou,Kenta Nakai* and Leyi Wei*.

    Bioinformatics, 2022. DOI:10.1093/bioinformatics/btac352. In press.


  • [31] Recent advances in the prediction of subcellular localization of proteins and related topics.

    Kenta Nakai* and Leyi Wei.

    Frontiers in Bioinformatics, 2022. In press.


  • [30] scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods

    Chichi Dai, Yi Jiang, Chenglin Yin, Ran Su, Xiangxiang Zeng, Quan Zou, Kenta Nakai*, Leyi Wei*.

    Nucleic Acids Research, 2022. DOI:10.1093/nar/gkac317. In press.


  • [29] Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on immunohistochemistry images

    Fengsheng Wang, Leyi Wei*.

    Bioinformatics, 2022. DOI: 10.1093/bioinformatics/btac123. In press.


  • [28] ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning

    Lesong Wei, Xiucai Ye*, Tetsuya Sakurai, Zengchao Mu, Leyi Wei*

    Bioinformatics, 2022. DOI: 10.1093/bioinformatics/btac006.


2021


  • [27] Accelerating Bioactive Peptide Discovery via Mutual Information-based Meta-learning

    Wenjia He, Yi Jiang, Junru Jin, Zhongshen Li, Jiaojiao Zhao, Balachandran Manavalan, Ran Su, Xin Gao*,Leyi Wei*.

    Briefings in Bioinformatics, 2021.


  • [26] iDNA-ABT:advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization

    Yingying Yu, Wenjia He, Junru Jin, Lizhen Cui, Rao Zeng*,Leyi Wei*.

    Bioinformatics, 2021. DOI: 10.1093/bioinformatics/btab677. In press.


  • [25] Iterative feature representation algorithm to improve the predictive performance of N7-methylguanosine sites

    Chichi Dai, Pengmian Feng, Lizhen Cui, Ran Su*, Wei Chen*, Leyi Wei*.

    Briefings in Bioinformatics, 2021. DOI: 10.1093/bib/bbaa278.


  • [24] Protein subcellular localization based on deep image features and criterion learning strategy

    Ran Su*, Linlin He, Tianling Liu, Xiaofeng Liu*, Leyi Wei*.

    Briefings in Bioinformatics, 2021. DOI: 10.1093/bib/bbaa313.


  • [23] PSSP-MVIRT: peptide secondary structure prediction based on a multi-view deep learning architecture

    Xiao Cao, Wenjia He, Zitan Chen, Yifan Li, Kexin Wang, Hongbo Zhang, Lesong Wei, Lizhen Cui, Ran Su*, Leyi Wei*.

    Briefings in Bioinformatics, 2021. DOI: 10.1093/bib/bbab203.


  • [22] Learning embedding features based on multi-sense-scaled attention architecture to improve the predictive performance of anticancer peptides

    Wenjia He, Yu Wang, Lizhen Cui, Ran Su*, Leyi Wei*.

    Bioinformatics, 2021. DOI: 10.1093/bioinformatics/btab560. In press


  • [21] ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism.

    L Wei, X Ye*, Y Xue, T Sakurai*, L Wei*.

    Briefings in Bioinformatics, 2021. DOI: 10.1093/bib/bbab041. In press


  • [20] Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network.

    J Liu, R Su, J Zhang, L Wei*.

    Briefings in Bioinformatics, 2021. DOI: 10.1093/bib/bbaa395. In press


  • [19] PepFormer: end-to-end Transformer-Based Siamese Network to Predict and Enhance Peptide Detectability Based on Sequence Only.

    Hao Cheng, Bing Rao, Lei Liu, Lizhen Cui, Guobao Xiao, Ran Su*, Leyi Wei*.

    Analytical Chemistry, 2021. 93(16):6481-6490

2020


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

    Zengyan Hong, Xiangxiang Zeng*, Leyi Wei*, Xiangrong Liu*.

    Bioinformatics, 2020. 36(4): 1037-1043.


  • [17] Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework

    Leyi Wei*, Wenjia He, Adeel Malik, Ran Su, Lizhen Cui, Balachandran Manavalan*.

    Briefings in Bioinformatics, 2020. DOI:10.1093/bib/bbaa275.


  • [16] EP3: an ensemble predictor that accurately identifies type III secreted effectors.

    Jing Li, Leyi Wei, Fei Guo*, Quan Zou*.

    Briefings in bioinformatics, 2020. 22(2), 1918-1928.


  • [15] Identification of expression signatures for non-small-cell lung carcinoma subtype classification.

    Ran Su*, Jiahang Zhang, Xiaofeng Liu*, Leyi Wei*.

    Bioinformatics, 2020. 36(2), 339-346.

2019

  • [14] Predicting drug-induced hepatotoxicity based on biological feature maps and diverse classification strategies.

    Ran Su*, Huichen Wu, Xinyi Liu, Leyi Wei*.

    Briefings in bioinformatics, 2019. 22(1), 428-437.


  • [13] Iterative feature representations improve N4-methylcytosine site prediction.

    Leyi Wei, Ran Su, Shasha Luan, Zhijun Liao, Balachandran Manavalan*, Quan Zou*, Xiaolong Shi*.

    Bioinformatics, 2019. 35(23), 4930-4937.


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

    Bing Rao, Chen Zhou, Guoying Zhang*, Ran Su*, Leyi Wei*.

    Briefings in bioinformatics, 2019. 21(5), 1846-1855.


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

    Leyi Wei, Chen Zhou, Ran Su*, Quan Zou*.

    Bioinformatics, 2019. 35(21), 4272-4280.


  • [10] PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact.

    Fuyi Li*, Cunshuo Fan, Tatiana T Marquez-Lago, André Leier, Jerico Revote, Cangzhi Jia, Yan Zhu, A Ian Smith, Geoffrey I Webb, Quanzhong Liu*, Leyi Wei*, Jian Li, Jiangning Song*.

    Briefings in bioinformatics, 2019. 21(3), 1069-1079.


  • [9] Meta-GDBP: a high-level stacked regression model to improve anticancer drug response prediction.

    Ran Su, Xinyi Liu, Guobao Xiao*, Leyi Wei*.

    Briefings in bioinformatics, 2019. 21(3), 996-1005.


  • [8] MinE-RFE: determine the optimal subset from RFE by minimizing the subset-accuracy–defined energy.

    Ran Su, Xinyi Liu, Leyi Wei*.

    Briefings in bioinformatics, 2019. 21(2), 687-698.

2018


  • [7] mAHTPred: a sequence-based meta predictor for improving the prediction of antihypertensive peptides using effective feature representation.

    B. Manavalan, S. Basith, T.H. Shin, Leyi Wei*, G. Lee*.

    Bioinformatics, 2018. 35(16), 2757-2765.


  • [6] Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools.

    Ran Su, Jie Hu, Quan Zou, Balachandran Manavalana*, Leyi Wei*.

    Briefings in Bioinformatics, 2018. 35(16), 2757-2765.


  • [5] Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms.

    Leyi Wei, Jie Hu, Fuyi Li, Jiangning Song*, Ran Su*, and Quan Zou*.

    Briefings in bioinformatics, 2018. 21(1), 106-119.


  • [4] 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*, and Quan Zou*.

    Bioinformatics, 2018. 35(8), 1326-1333.


  • [3] CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning.

    Xiaoli Qiang, Chen Zhou, Xiucai Ye, Pufeng Du, Ran Su*, and Leyi Wei*.

    Briefings in bioinformatics, 2018. 21(1), 11-23.


  • [2] 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*, and Ran Su*.

    Bioinformatics, 2018. 34(23), 4007-4016.

2017


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

    Leyi Wei, Jijun Tang, and Quan Zou*.

    Information Sciences, 2017. 384:135-144.