# These authors contributed equally to the paper as first authors
* To whom correspondence should be addressed
 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.
 Recent advances in the prediction of subcellular localization of proteins and related topics.
Kenta Nakai* and Leyi Wei.
Frontiers in Bioinformatics, 2022. In press.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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
 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
 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
 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
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
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