代表论文：

1. 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)
2. 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)
3. 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)


4. 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)
5. 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)
6. 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)
7. 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)
8. 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)
9. 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)
10. 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)
11. 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)
12. 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)
13. StackCPPred: A Stacking and Pairwise Energy Content based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency. Bioinformatics. 2020, 36(10):3028-3034. (code)
14. Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics. 2019, 35(23): 4930-4937. (SCI, IF2017=5.481, PMID:31099381)(web server)
15. PaGeFinder: Quantitative Identification of Spatiotemporal Pattern Genes. Bioinformatics. 2012, 28(11):1544-1545. (SCI, IF2017=5.481, PMID:22492640)(Web Server)(BibTeX, EndNote)
16. 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)
17. Identification of Sub-Golgi Protein Localization by Use of Deep Representation Learning Features. Bioinformatics. DOI：10.1093/bioinformatics/btaa1074 (web server)


18. 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
19. Revisiting genome-wide association studies from statistical modelling to machine learning. Briefings in Bioinformatics. Doi: 10.1093/bib/bbaa263
20. An in silico approach to identification, categorization and prediction of nucleic acid binding proteins. Briefings in Bioinformatics. 2021,22(3):bbaa171 (web sites)
21. Survey of MapReduce Frame Operation in Bioinformatics. Briefings in Bioinformatics. 2014,15(4): 637-647. (SCI, IF2017=6.302, PMID: 23396756)(BibTeX, EndNote)
22. 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)
23. 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)
24. 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)
25. Computational methods for identifying the critical nodes in biological networks. Briefings in Bioinformatics. 2020, 21(2): 486-497.(SCI, IF2017=6.302, PMID:30753282)
26. Transcription factors-DNA interactions in rice: identification and verification. Briefings in Bioinformatics. 2020, 21(3): 946-956. (SCI, IF2017=6.302, PMID:31091308)
27. Clustering and Classification Methods for Single-cell RNA-sequencing Data. Briefings in Bioinformatics. 2020, 21(4): 1196-1208(SCI, IF2017=6.302, PMID:31271412)
28. DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences. Briefings in Bioinformatics. 2021,22(3):bbaa159. (codes)
29. Critical evaluation of web-based prediction tools for human protein subcellular localization. Briefings in Bioinformatics. 2020,21(5):1628-1640. (web server)
30. 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)
31. EP3: An ensemble predictor that accurately identifies type III secreted effectors. Briefings in Bioinformatics. 2021,22(2):1918-1928. (web site)
32. A Spectral Clustering with Self-weighted Multiple Kernel Learning Method for single-cell RNA-seq Data. Briefings in Bioinformatics. Doi: 10.1093/bib/bbaa216. (codes)
33. Goals and Approaches for Each Processing Step for Single-Cell RNA Sequencing Data. Briefings in Bioinformatics. Doi: 10.1093/bib/bbaa314. (codes)
34. VPTMdb: a viral post-translational modification database. Briefings in Bioinformatics. Doi:10.1093/bib/bbaa251. (web sites)
35. Application of Learning to Rank in Bioinformatics Tasks. Briefings in Bioinformatics. Doi:10.1093/bib/bbaa394
36. ITP-Pred: An interpretable method for predicting therapeutic peptides with fused features lowdimension represention. Briefings in Bioinformatics. Doi: 10.1093/bib/bbaa367
37. SubLocEP: A novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning. Briefings in Bioinformatics. doi:10.1093/bib/bbaa401. (web site)
38. GutBalance: a server for the human gut microbiome-based disease prediction and biomarker discovery with compositionality addressed. Briefings in Bioinformatics. doi: 10.1093/bib/bbaa436. (web site)
39. Prediction of RNA-binding protein and alternative splicing event associations during epithelial-mesenchymal transition based on inductive matrix completion. Briefings in Bioinformatics. Doi: 10.1093/bib/bbaa440. (codes)
40. Anti-Cancer Peptide Prediction with Deep Representation Learning Features. Briefings in Bioinformatics. DOI: 10.1093/bib/bbab008. (codes)
41. A comprehensive overview and critical evaluation of gene regulatory network inference technologies. Briefings in Bioinformatics. Doi: 10.1093/bib/bbab009
42. A Comprehensive Review of the Imbalance Classification of Protein Post-Translational Modifications. Briefings in Bioinformatics. Doi: 10.1093/bib/bbab089. (web site)
43. DisBalance: a platform to automatically build balance-based disease prediction models and discover microbial biomarkers from microbiome data. Briefings in Bioinformatics. Doi: 10.1093/bib/bbab094 (web)
44. Critical downstream analysis steps for single-cell RNA sequencing data. Briefings in Bioinformatics. Doi: 10.1093/bib/bbab105 (codes)
45. MMFGRN: a multi-source multi-model fusion method for Gene Regulatory Network reconstruction. Briefings in Bioinformatics. DOI:10.1093/bib/bbab166. (codes and data)
46. The accurate prediction and characterization of cancerlectin by a combined machine learning and GO analysis. Briefings in Bioinformatics. Doi:10.1093/bib/bbab227


47. Decision Tree for Sequences. IEEE Transactions on Knowledge and Data Engineering. Doi: 10.1109/TKDE.2021.3075023 (codes)
48. SgRNA-RF: identification of SgRNA on-target activity with imbalanced datasets. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Accepted. (web server)
49. 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)
50. 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)
51. 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)
52. 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)
53. 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)
54. Significance-Based Essential Protein Discovery. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Doi:10.1109/TCBB.2020.3004364
55. Advanced machine learning techniques for bioinformatics. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2019,16(4):1182-1183(SCI, IF2017=2.428)
56. CRCF: A Method of Identifying Secretory Proteins of Malaria Parasites. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Accepted. (web server)
57. 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)
58. 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)


59. An overview of SNP interactions in genome-wide association studies. Briefings in Functional Genomics. 2015, 14(2):143-155. (SCI, IF2017=3.783,PMID: 25241224)(BibTeX, EndNote)
60. Similarity computation strategies in the microRNA-disease network: A Survey. Briefings in Functional Genomics. 2016, 15(1): 55-64. (SCI, IF2017=3.783,PMID: 26134276) most cited articles in this journal (BibTeX, EndNote)
61. Research Progress in Protein Post-Translational Modification Site Prediction. Briefings in Functional Genomics. 2018, 18(4): 220-229. (SCI, IF2017=3.783, PMID: 30576418)
62. Deep learning in omics: a survey and guideline. Briefings in Functional Genomics. 2019, 18(1): 41-57 (SCI, IF2017=3.783, PMID: 30265280)
63. Prediction of tumor metastasis from sequencing data in the era of genome sequencing. Briefings in Functional Genomics. 2019, 18(6): 412-418 (SCI, IF2017=3.783, PMID: 31204784)
64. Machine learning and its applications in plant molecular studies. Briefings in Functional Genomics. 2020, 19(1): 40-48
65. Identifying Cell Types to Interpret scRNA-seq Data: How, Why, and More Possibilities. Briefings in Functional Genomics. 2020, 19(4):286-291
66. Prediction of bio-sequence modifications and the associations with diseases. Briefings in Functional Genomics. 2021, 20(1): 1-18 (data)Editor's Choice
67. Sequence representation approaches for sequence-based protein prediction tasks that use deep learning. Briefings in Functional Genomics. 2021, 20(1): 61-63


68. mAML: an automated machine learning pipeline with a microbiome repository for human disease classification. DATABASE-The Journal of Biological Databases and Curation. 2020, 2020: baaa050. (web)
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. Local-DPP: An Improved DNA-binding Protein Prediction Method by Exploring Local Evolutionary Information. Information Sciences. 2017, 384:135-144. (SCI, IF2017=4.305)(web server)(BibTeX, EndNote)
71. 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)
72. 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
73. 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)