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# These authors contributed equally to the paper as first authors
* To whom correspondence should be addressed
[37] Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks
Yu Wang, Chao Pang, Yuzhe Wang, Junru Jin, Jingjie Zhang, Xiangxiang Zeng, Ran Su, Quan Zou*, and Leyi Wei*.
Nature Communications, 2023. DOI: 10.1038/s41467-023-41698-5
[36] ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides
Zhongshen Li#, Junru Jin#, Yu Wang, Wentao Long, Yuanhao Ding, Haiyan Hu, and Leyi Wei*.
Bioinformatics, 2023. DOI: 10.1093/bioinformatics/btad108. (39)3.
[35] Explainable deep hypergraph learning modeling the peptide secondary structure prediction
Yi Jiang#, Ruheng Wang#, Jiuxin Feng, Junru Jin, Sirui Liang, Zhongshen Li, Yingying Yu, Anjun Ma, Ran Su, Quan Zou, Qin Ma* and Leyi Wei*.
Advanced Science, 2023. DOI: 10.1002/advs.202206151. (10)11.
[34] DeepBIO: An automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation, and visualization analysis
Ruheng Wang#, Yi Jiang#, Junru Jin#, Chenglin Yin#, Haoqing Yu, Fengsheng Wang, Jiuxin Feng, Ran Su, Kenta Nakai, Quan Zou*, Leyi Wei*.
Nucleic Acids Research, 2023. DOI: 10.1093/nar/gkad055. (51)7, 3017-3029.
[33] iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations
Junru Jin, Yingying Yu, Ruheng Wang, Xin Zeng, Chao Pang, Yi Jiang, Zhongshen Li, Yutong Dai, Ran Su, Quan Zou, Kenta Nakai* and Leyi Wei*.
Genome Biology, 2022. DOI: 10.1186/s13059-022-02780-1. (23)1.
[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. (38)13, 3351-3360.
[31] Recent advances in the prediction of subcellular localization of proteins and related topics.
Kenta Nakai* and Leyi Wei.
Frontiers in Bioinformatics, 2022. DOI: 10.3389/fbinf.2022.910531. (2), 910531.
[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. (50)9, 4877-4899.
[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. (38)9, 2602-2611.
[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. (38)6, 1514-1524.
[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. DOI: 10.1093/bib/bbab499. (23)1.
[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. (37)24, 4603-4610.
[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. (22)4.
[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. (22)4.
[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)6.
[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. (10), 104923-104933.
[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. (10), 104923-104933.
[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. (22)5.
[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.
[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. (22)4.
[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.
[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.
[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.
[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.
Room 404,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, Shandong, China
Post code：250101
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