代表论文：

1. webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study. Nucleic Acids Research. Doi: 10.1093/nar/gkab957. (web site)
2. A comprehensive overview and evaluation of circular RNA detection tools. PLoS Computational Biology. 2017,13(6): e1005420 (SCI, IF2017=3.955, PMID: 28594838) (data and codes)(BibTeX, EndNote)
3. Details in the evaluation of circular RNA detection tools: Reply to Chen and Chuang. PLoS Computational Biology. 2019, 15(4): e1006916 (SCI, IF2017=3.955, PMID: 31022173)
4. Gene2vec: Gene Subsequence Embedding for Prediction of Mammalian N6‐Methyladenosine Sites from mRNA. RNA. 2019, 25(2): 205-218 (SCI, IF2017=4.490, PMID: 30425123) (web server)(BibTeX, EndNote)


5. HAlign: Fast Multiple Similar DNA/RNA Sequence Alignment Based on the Centre Star Strategy. Bioinformatics. 2015,31(15): 2475-2481. (SCI, IF2017=5.481, PMID: 25812743) (Software)(该软件被OMICTOOLS推荐)(BibTeX, EndNote)
6. Basic polar and hydrophobic properties are the main characteristics that affect the binding of transcription factors to methylation sites. Bioinformatics. 2020,36(15):4263-4268 (Supplementary data)
7. PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning. Bioinformatics. 2019, 35(21):4272-4280. (SCI, IF2017=5.481, PMID:30994882) (web server)
8. PPTPP: A novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning. Bioinformatics. 2020, 36(13): 3982-3987 (codes and data)
9. Tumor Origin Detection with Tissue-Specific miRNA and DNA methylation Markers. Bioinformatics. 2018, 34(3): 398-406. (SCI, IF2017=5.481, PMID:29028927) (web server) High impact research from Bioinformatics (BibTeX, EndNote)
10. Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species. Bioinformatics. 2019, 35(8): 1326-1333. (SCI, IF2017=5.481, PMID: 30239627)(web server)
11. O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique. Bioinformatics. 2018, 34(12): 2029-2036. (SCI, IF2017=5.481, PMID:29420699)(web server)(该软件被OMICTOOLS推荐)(BibTeX, EndNote)
12. Prediction of potential disease-associated microRNAs using structural perturbation method. Bioinformatics. 2018, 34(14): 2425-2432.(SCI, IF2017=5.481, PMID:29490018)(codes)(BibTeX, EndNote)
13. 4mCPred: Machine Learning Methods for DNA N⁴-methylcytosine sites Prediction. Bioinformatics. 2019, 35(4): 593-601 (SCI, IF2017=5.481, PMID: 30052767)(web server)(该软件被OMICTOOLS推荐)
14. StackCPPred: A Stacking and Pairwise Energy Content based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency. Bioinformatics. 2020, 36(10):3028-3034. (code)
15. Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics. 2019, 35(23): 4930-4937. (SCI, IF2017=5.481, PMID:31099381)(web server)
16. PaGeFinder: Quantitative Identification of Spatiotemporal Pattern Genes. Bioinformatics. 2012, 28(11):1544-1545. (SCI, IF2017=5.481, PMID:22492640)(Web Server)(BibTeX, EndNote)
17. BP4RNAseq: a babysitter package for retrospective and newly generated RNA-seq data analyses using both alignment-based and alignment-free quantification methods. Bioinformatics. 2021, 37(9):1319-1321. (codes)
18. Identification of Sub-Golgi Protein Localization by Use of Deep Representation Learning Features. Bioinformatics. 2020, 36(24):5600-5609 (web server)
19. DeepAc4C: A convolutional neural network model with hybrid features composed of physico-chemical patterns and distributed representation information for identification of N4 acetylcytidine in mRNA. Bioinformatics. Doi: 10.1093/bioinformatics/btab611. (web server)


20. Sequence clustering in bioinformatics: an empirical study. Briefings in Bioinformatics. 2020,21(1): 1-10 (SCI, IF2017=6.302, PMID: 30239587)(data) Highly Cited Articles from Briefings in Bioinformatics
21. Revisiting genome-wide association studies from statistical modelling to machine learning. Briefings in Bioinformatics. 2021, 22(4): bbaa263
22. An in silico approach to identification, categorization and prediction of nucleic acid binding proteins. Briefings in Bioinformatics. 2021,22(3):bbaa171 (web sites)
23. Survey of MapReduce Frame Operation in Bioinformatics. Briefings in Bioinformatics. 2014,15(4): 637-647. (SCI, IF2017=6.302, PMID: 23396756)(BibTeX, EndNote)
24. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Briefings in Bioinformatics. 2016,17(2):193-203.(SCI, IF2017=6.302, PMID:26059461)(BibTeX, EndNote)
25. Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms. Briefings in Bioinformatics. 2020,21(1): 106-119. (SCI, IF2017=6.302, PMID:30383239)(web server)
26. HITS-PR-HHblits: Protein remote homology detection by combining pagerank and hyperlink-induced topic search. Briefings in Bioinformatics. 2020,21(1): 298-308.(SCI, IF2017=6.302, PMID:30403770)(web server)
27. Computational methods for identifying the critical nodes in biological networks. Briefings in Bioinformatics. 2020, 21(2): 486-497.(SCI, IF2017=6.302, PMID:30753282)
28. Transcription factors-DNA interactions in rice: identification and verification. Briefings in Bioinformatics. 2020, 21(3): 946-956. (SCI, IF2017=6.302, PMID:31091308)
29. Clustering and Classification Methods for Single-cell RNA-sequencing Data. Briefings in Bioinformatics. 2020, 21(4): 1196-1208(SCI, IF2017=6.302, PMID:31271412)
30. DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences. Briefings in Bioinformatics. 2021,22(3):bbaa159. (codes)
31. Critical evaluation of web-based prediction tools for human protein subcellular localization. Briefings in Bioinformatics. 2020,21(5):1628-1640. (web server)
32. Predicting Disease-associated Circular RNAs Using Deep Forests Combined with Positive-Unlabeled Learning Methods. Briefings in Bioinformatics. 2020, 21(4): 1425-1436.(data and code)
33. EP3: An ensemble predictor that accurately identifies type III secreted effectors. Briefings in Bioinformatics. 2021,22(2):1918-1928. (web site)
34. A Spectral Clustering with Self-weighted Multiple Kernel Learning Method for single-cell RNA-seq Data. Briefings in Bioinformatics. 2021, 22(4): bbaa216. (codes)
35. Goals and Approaches for Each Processing Step for Single-Cell RNA Sequencing Data. Briefings in Bioinformatics. 2021, 22(4): bbaa314. (codes)
36. VPTMdb: a viral post-translational modification database. Briefings in Bioinformatics. 2021, 22(4): bbaa251. (web sites)
37. Application of Learning to Rank in Bioinformatics Tasks. Briefings in Bioinformatics. 2021, 22(5):bbaa394
38. SubLocEP: A novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning. Briefings in Bioinformatics. 2021, 22(5): bbaa401. (web site)
39. GutBalance: a server for the human gut microbiome-based disease prediction and biomarker discovery with compositionality addressed. Briefings in Bioinformatics.2021, 22(5):bbaa436. (web site)
40. Prediction of RNA-binding protein and alternative splicing event associations during epithelial-mesenchymal transition based on inductive matrix completion. Briefings in Bioinformatics. 2021, 22(5): bbaa440. (codes)
41. Anti-Cancer Peptide Prediction with Deep Representation Learning Features. Briefings in Bioinformatics. 2021, 22(5): bbab008. (codes)
42. A comprehensive overview and critical evaluation of gene regulatory network inference technologies. Briefings in Bioinformatics. 2021,22(5):bbab009
43. A Comprehensive Review of the Imbalance Classification of Protein Post-Translational Modifications. Briefings in Bioinformatics. 2021, 22(5):bbab089. (web site)
44. DisBalance: a platform to automatically build balance-based disease prediction models and discover microbial biomarkers from microbiome data. Briefings in Bioinformatics. 2021, 22(5):bbab094 (web)
45. Critical downstream analysis steps for single-cell RNA sequencing data. Briefings in Bioinformatics. 2021,22(5):bbab105 (codes)
46. MMFGRN: a multi-source multi-model fusion method for Gene Regulatory Network reconstruction. Briefings in Bioinformatics. 2021, 22(6):bbab166s. (codes and data)
47. High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method. Briefings in Bioinformatics. 2021, 22(6): bbab273
48. Matrix factorization-based data fusion for the prediction of RNA-binding protein and alternative splicing event associations during epithelial-mesenchymal transition. Briefings in Bioinformatics. 2021, 22(6):bbab332. (codes)
49. A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data. Briefings in Bioinformatics. DOI: 10.1093/bib/bbab345
50. Characterizing Viral circRNAs and their Application in Identifying CircRNAs in Viruses. Briefings in Bioinformatics. Doi: 10.1093/bib/bbab404. (web server)
51. Distant metastasis identification based on optimized graph representation of gene interaction patterns. Briefings in Bioinformatics. Accepted. (codes)
52. NmRF: Identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences. Briefings in Bioinformatics. Accepted. (web)
53. DeepCap-Kcr: accurate identification and investigation of protein lysine crotonylation sites based on capsule network. Briefings in Bioinformatics. Accepted. (codes)
54. A novel fast multiple nucleotide sequence alignment method based on FM-index. Briefings in Bioinformatics. Accepted. (FMAlign codes)
55. A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape. Briefings in Bioinformatics. Accepted.(codes)


56. Decision Tree for Sequences. IEEE Transactions on Knowledge and Data Engineering. Doi: 10.1109/TKDE.2021.3075023 (codes)
57. SgRNA-RF: identification of SgRNA on-target activity with imbalanced datasets. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Accepted. (web server)
58. Fast prediction of protein methylation sites using a sequence-based feature selection technique. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2019,16(4):1264-1273. (SCI, IF2015=1.609, PMID:28222000)(web server)(该软件被OMICTOOLS推荐)
59. Prediction and validation of disease genes using HeteSim Scores. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2017, 14(3):687-695. (SCI, IF2017=2.428, PMID:26890920)(Codes and Data)(BibTeX, EndNote)
60. Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2017, 14(4): 905-915.(Web Server)(SCI, IF2015=1.609, PMID:27076459)(BibTeX, EndNote)
61. Improved and Promising Identification of Human MicroRNAs by Incorporating a High-quality Negative Set. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2014, 11(1):192-201 (SCI, IF2017=2.428, PMID: 24216114)(Software)(BibTeX, EndNote)
62. Protein Complexes Identification with Family-Wise Error Rate Control. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2020, 17(6): 2062-2073. (SCI, IF2017=2.428, PMID:31027047)
63. Significance-Based Essential Protein Discovery. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Doi:10.1109/TCBB.2020.3004364
64. Advanced machine learning techniques for bioinformatics. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2019,16(4):1182-1183(SCI, IF2017=2.428)
65. CRCF: A Method of Identifying Secretory Proteins of Malaria Parasites. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Accepted. (web server)
66. PhosPred-RF: a novel sequence-based predictor for phosphorylation sites using sequential information only. IEEE Transactions on NanoBioscience. 2017, 16(4): 240-247. (SCI, IF2017=2.158, PMID:28166503) (web server)(BibTeX, EndNote)
67. Investigating maize yield-related genes in multiple omics interaction network data. IEEE Transactions on NanoBioscience. 2020, 19(1): 142-151 (SCI, IF2017=2.158, PMID:31170079)


68. Prediction of bio-sequence modifications and the associations with diseases. Briefings in Functional Genomics. 2021, 20(1): 1-18 (data)Editor's Choice
69. CPPred-RF: a sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency. Journal of Proteome Research.2017, 16(5):2044-2053. (SCI, IF2018=3.780)(web server)(入选ACS出版社庆祝中科院建院70周年Highlight文章)
70. Prediction of human protein subcellular localization using deep learning. Journal of Parallel and Distributed Computing. 2018, 117: 212-217 (SCI, IF2017=1.815) most cited articles in this journal (BibTeX, EndNote)
71. Review and comparative analysis of machine learning-based phage virion protein identification methods. BBA - Proteins and Proteomics. 2020,1868(6):140406 Featured in the BBA Collection on "Viruses" and "Artificial Intelligence for biochemistry and molecular biology: AI is learning to help"
72. Multiple Sequence Alignment Based on a Suffix Tree and Center-Star Strategy: A Linear Method for Multiple Nucleotide Sequence Alignment on Spark Parallel Framework. Journal of Computational Biology. 2017, 24(12): 1230-1242 (SCI, IF2017=1.191, PMID: 29116822) (codes)(该软件被OMICTOOLS推荐)(BibTeX, EndNote)