2024年

[1] Zhang Shengli*, Zhao Ya, Liang Yunyun. AACFlow: An end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides. Bioinformatics, 2024, btae142.

[2] Zhang Shengli*, Xu Yujie, Liang Yunyun. TMSC-m7G: A transformer architecture based on multi-sense-scaled embedding features and convolutional neural network to identify RNA N7-methylguanosine sites. Computational and Structural Biotechnology Journal. 2024, 23, 129-139.

[3] Zhang Shengli*, Zhao Ya, Liang Yunyun. AMP-EF: An Ensemble Framework of Extreme Gradient Boosting and Bidirectional Long Short-Term Memory Network for Identifying Antimicrobial Peptides. MATCH Communications in Mathematical and in Computer Chemistry. 2024, 91, 109-131.

2023年

[1] Zhang Shengli*, Jing Yuanyuan, PreVFs-RG: A deep hybrid model for identifying virulence factors based on residual block and gated recurrent unit. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2023, 20(3), 1926-1934.

[2] S. Zhang*, Y. Xu, Y. Jing and Y. Liang, TNFIPs-Net: A deep learning model based on multi-feature fusion for prediction of TNF-α inducing epitopes, 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 978-983, doi: 10.1109/BIBM58861.2023.10385839. 05-08 December 2023.

[3] Zhang Shengli*, Li Xinjie, Shi Hongyan, Jing Yuanyuan, Liang Yunyun, Zhang Yusen. Fer-COCL: a novel method based on multiple deep learning algorithms for identifying fertility-related proteins. MATCH Communications in Mathematical and in Computer Chemistry. 2023, 90, 537-559.

[4] Jing Yuanyuan, Zhang Shengli*, Wang Houqiang. DapNet-HLA: Adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites. Analytical Biochemistry. 2023, 666, 115075.

2022年

[1] Li Xinjie, Zhang Shengli*, Shi Hongyan. An improved residual network using deep fusion for identifying RNA 5-Methylcytosine sites. Bioinformatics. 2022, 38(18), 4271–4277.

[2]Shi Hongyan, Zhang Shengli*, Li Xinjie. R5hmCFDV: computational identification of RNA 5-hydroxymethylcytosine based on deep feature fusion and deep voting. Briefings in Bioinformatics, 2022, 23(5), bbac341.

[3] Zhang Shengli*, Li Xinjie. Pep-CNN: An improved convolutional neural network for predicting therapeutic peptides. Chemometrics and Intelligent Laboratory Systems. 2022, 221, 104490.

[4]Shi Hongyan, Zhang Shengli*, Accurate Prediction of Anti‑hypertensive Peptides Based on Convolutional Neural Network and Gated Recurrent unit. Interdisciplinary Sciences: Computational Life Sciences. 2022, 14, 879–894.

[5] Qiao Huijuan, Zhang Shengli*, Wang Jinyue. iPro-GAN: A novel model based on generative adversarial learning for identifying promoters and their strength[J]. Computer Methods and Programs in Biomedicine, 2022, 215. 106625.

[6] Zhang Shengli*, Wang Jinyue, Li Xinjie, Liang Yunyun. M6A-GSMS: Computational identification of N6methyladenosine sites with GBDT and stacking learning in multiple species[J]. Journal of Biomolecular Structure and Dynamics, 2022, 40:22, 12380-12391.

[7] Yao Yingying, Zhang Shengli*, Xue Tian. Integrating LASSO feature selection and soft voting classifier to identify origins of replication sites. Current Genomics, 2022, 23(2), 83-93.

[8] Zhang Shengli*, Yao Yingying, Wang Jiesheng. Identification of DNA N4-methylcytosine sites based on multi-source features and gradient boosting decision tree. Analytical Biochemistry, 2022, 652: 114746.

1. iR5hmcSC: Identifying RNA 5-hydroxymethylcytosine with multiple features based on stacking learning. Computational Biology and Chemistry. 2021
2. PA-PseU: An incremental passive-aggressive based method for identifying RNA pseudouridine sites via Chou's 5-steps rule.  Chemometrics and Intelligent Laboratory Systems. 2021
3. iDHS-DASTS: identifying DNase I hypersensitive sites based on LASSO and stacking learning. Molecular Omics. 2021
4. Use Chou’s 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting. Molecular Genetics and Genomics. 2020
5. Zhang Shengli*; Yu Qianhao; He Haoran; Zhu Fu; Wu Panjing; Gu Lingzhi; Jiang Sijie. iDHS-DSAMS: Identifying DNase I hypersensitive sites based on the dinucleotide property matrix and ensemble bagged tree. Genomics. 2020, 112, 1282-1289. (SCI, IF=6.025) 
6. Zhang Shengli*; Yunyun Liang*. Integrating Second-order Moving Average and Over-sampling Algorithm to Predict Apoptosis Protein Subcellular Localization. Current Bioinformatics. 2020, 15, 1-11. 
7. Zhang, Shengli*; Zhang, Tongtong; Liu, Chang. Prediction of apoptosis protein subcellular localization via heterogeneous features and hierarchical extreme learning machine. SAR and QSAR in Environmental Research, 2019.3.4, 30(3): 209-228. 
8. Zhou Cong; Liu Sanyang; Zhang Shengli*. Identification of amyloidogenic peptides via optimized integrated features space based on physicochemical properties and PSSM. Analytical Biochemistry, 2019.7.13, 583, 113362. 
9. Shengli Zhang*, Kaiwen Yang, Yuqing Lei, Kang Song. iRSpot-DTS: predict recombination spots by incorporating the dinucleotide based spare-cross covariance information into Chou's pseudo components. Genomics. 2019. 
10. Shengli Zhang*, Xin Duan. Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC. Journal of Theoretical Biology, 2018, 437, 239-250.  (ESI高被引论文)
11. Shengli Zhang*, Yunyun Liang. Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC.Journal of Theoretical Biology, 2018, 457, 163-169. 
12. Shengli Zhang*, Jin Jin, Prediction of Protein Subcellular Localization by Using λ-order Factor and Principal Component Analysis. Letters in Organic Chemistry, 2017, 14(3): 717-724. 
13. Shengli Zhang*. Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou's general PseAAC. Chemometrics and Intelligent Laboratory Systems. 142: 28-35. 2015. 
14. Shengli Zhang*, Yunyun Liang, Zhenguo Bai. A Novel Reduced Triplet Composition Based Method to Predict Apoptosis Protein Subcellular Localization, MATCH Communications in Mathematical and in Computer Chemistry. 73(2),559-571, 2015. 
15. Yunyun Liang, Shengli Zhang. Prediction of apoptosis protein subcellular localization by fusing two different descriptors based on evolutionary information. Acta Biotheoretica, 2018. 
16. Shuyan Ding, Shengli Zhang, A Gram-negative Bacterial Secreted Protein Types Prediction Method Based on PSI-BLAST Profile. BioMed Research International, 2016. ID: 3206741.
17. Yunyun Liang, Sanyang Liu, Shengli Zhang, Prediction of protein structural classes for low-similarity sequences based on consensus sequence and segmented PSSM. CMMM. 2015.
18. Shengli Zhang , Yusen Zhang , Ivan Gutman. Analysis of DNA Sequences Based on the Fuzzy Integral. MATCH Communications in Mathematical and in Computer Chemistry. 2013. 70(1): 417-430. (SCI, IF=2.161)
19. Yuan X, Zhang J, Zhang S, Yu G, Wang Y. Comparative Analysis of Methods for Identifying Recurrent Copy Number Alterations in Cancer. PLoS ONE 2012, 7(12): e52516. (SCI, IF=4.092) 
20. Xiguo Yuan, Junying Zhang, Liying Yang, Shengli Zhang, Baodi Chen, Yaojun Geng, Yue Wang. TAGCNA: A Method to Identify Significant Consensus Events of Copy Number Alterations in Cancer. PLoS ONE 2012, 7(7): e41082. (SCI, IF=4.092)
21.  Shengli Zhang, Feng Ye, Xiguo Yuan. Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM. Journal of Biomolecular Structure and Dynamics. 2012, 29(6): 634-642.  SCI ( IF: 4.986) 
22.  Shuyan Ding, Shengli Zhang, Yang Li, Tianming Wang.  A novel protein structural classes prediction method based on predicted secondary structure. Biochimie. 2012, 94(5): 1166-1171. SCI (IF: 3.897)   software.rar   ASTRAL_training.xls  ASTRAL_test.xls   
23. Shengli Zhang, Tianming Wang. Utilization of the n-gram modeling to construct phylogenetic tree for proteins. Journal of Computational and Theoretical Nanoscience. 2011, 8(11): 2227-2232.  SCI ( IF: 0.843)   
24. Shengli Zhang, Shuyan Ding, Tianming Wang. High-accuracy prediction of protein structural class for low-similarity sequences based on predicted secondary structure. Biochimie, 2011, 93: 710-714.  SCI (IF: 3.897) 
25. Shengli Zhang, Tianming Wang.  Feature analysis of protein structure by using discrete Fourier transform and continuous wavelet transform.  Journal of Mathematical Chemistry, 2009, 46:562-568.   SCI ( IF：1.381) 
26. Shengli Zhang, Lianping Yang, Tianming Wang. Use  of  information  discrepancy  measure  to  compare  protein  secondary structures.  Journal of Molecular Structure-THEOCHEM, 2009, 909:102-  106.  SCI ( IF：1.216)
27. Shengli Zhang, Tianming Wang.  Phylogenetic Analysis of Protein Sequences based on Conditional LZ Complexity.  MATCH Commun. Math. Comput. Chem., 2010, 63:701-716.  SCI (IF：3.217)
28. Shengli Zhang, Tianming Wang.  A complexity-based method to compare RNA secondary structures and its application.  Journal of Biomolecular Structure and Dynamics, 2010, 28:247-258.  SCI (IF：1.124)    
29. Shengli Zhang, Tianming Wang.  A New Distance-based Approach for Phylogenetic Analysis of  Protein Sequences.  International Journal of Biology and Biomedical Engineering, 2009, 3:35-42.  
30. Shengli Zhang, Tianming Wang.  A Novel Alignment-Free Method for Phylogenetic Analysis of Protein Sequences.  Proceedings of the 10th WSEAS International Conference on Applied Computer Science(ACS'10, Morioka, Japan). 2010, 67-71. EI, ISTP.  
31. Gang Xu, Shengli Zhang, Yunyun Liang.  Using linear regression analysis for face recognition based on PCA and LDA.  Proceedings of International Conference on Computational Intelligence and Software Engineering (CiSE, 2009), Issue Date: 11-13 Dec. 2009, 1-4.  EI, ISTP.  
32. Ying Guo, Yanfang Wang, Shengli Zhang.  A Novel Way to Numerically Characterize DNA   Sequences and Its Application.  International Journal of Quantum Chemistry. 2011, 111: 3971-3979.   SCI ( IF：1.315)