| Predicting diabetes mellitus with machine learning techniques Q Zou, K Qu, Y Luo, D Yin, Y Ju, H Tang Frontiers in genetics 9, 515, 2018 | 763 | 2018 |
| iRNA-PseU: Identifying RNA pseudouridine sites W Chen, H Tang, J Ye, H Lin, KC Chou Molecular Therapy-Nucleic Acids 5, 2016 | 307 | 2016 |
| iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition CJ Zhang, H Tang, WC Li, H Lin, W Chen, KC Chou Oncotarget 7 (43), 69783, 2016 | 189 | 2016 |
| iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators CQ Feng, ZY Zhang, XJ Zhu, Y Lin, W Chen, H Tang, H Lin Bioinformatics 35 (9), 1469-1477, 2019 | 188 | 2019 |
| Identifying sigma70 promoters with novel pseudo nucleotide composition H Lin, ZY Liang, H Tang, W Chen IEEE/ACM transactions on computational biology and bioinformatics 16 (4 …, 2017 | 179 | 2017 |
| HBPred: a tool to identify growth hormone-binding proteins H Tang, YW Zhao, P Zou, CM Zhang, R Chen, P Huang, H Lin International Journal of Biological Sciences 14 (8), 957, 2018 | 175 | 2018 |
| Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique H Tang, W Chen, H Lin Molecular BioSystems 12 (4), 1269-1275, 2016 | 172 | 2016 |
| Risk prediction of diabetes: big data mining with fusion of multifarious physical examination indicators H Yang, Y Luo, X Ren, M Wu, X He, B Peng, K Deng, D Yan, H Tang, ... Information Fusion 75, 140-149, 2021 | 153 | 2021 |
| Identification of secretory proteins in mycobacterium tuberculosis using pseudo amino acid composition H Yang, H Tang, XX Chen, CJ Zhang, PP Zhu, H Ding, W Chen, H Lin BioMed research international 2016, 2016 | 139 | 2016 |
| Identification of hormone binding proteins based on machine learning methods JX Tan, SH Li, ZM Zhang, CX Chen, W Chen, H Tang, H Lin Math. Biosci. Eng 16 (4), 2466-2480, 2019 | 136 | 2019 |
| Identification of bacterial cell wall lyases via pseudo amino acid composition XX Chen, H Tang, WC Li, H Wu, W Chen, H Ding, H Lin BioMed research international 2016, 2016 | 126 | 2016 |
| Pro54DB: a database for experimentally verified sigma-54 promoters ZY Liang, HY Lai, H Yang, CJ Zhang, H Yang, HH Wei, XX Chen, ... Bioinformatics 33 (3), 467-469, 2017 | 120 | 2017 |
| MethyRNA: a web server for identification of N6-methyladenosine sites W Chen, H Tang, H Lin Journal of Biomolecular Structure and Dynamics 35 (3), 683-687, 2017 | 119 | 2017 |
| Prediction of cell-penetrating peptides with feature selection techniques H Tang, ZD Su, HH Wei, W Chen, H Lin Biochemical and biophysical research communications 477 (1), 150-154, 2016 | 97 | 2016 |
| Sequence-based predictive modeling to identify cancerlectins HY Lai, XX Chen, W Chen, H Tang, H Lin Oncotarget 8 (17), 28169, 2017 | 88 | 2017 |
| Site-divergent delivery of terminal propargyls to carbohydrates by synergistic catalysis RZ Li, H Tang, L Wan, X Zhang, Z Fu, J Liu, S Yang, D Jia, D Niu Chem 3 (5), 834-845, 2017 | 81 | 2017 |
| IonchanPred 2.0: a tool to predict ion channels and their types YW Zhao, ZD Su, W Yang, H Lin, W Chen, H Tang International Journal of Molecular Sciences 18 (9), 1838, 2017 | 62 | 2017 |
| A two-step discriminated method to identify thermophilic proteins H Tang, RZ Cao, W Wang, TS Liu, LM Wang, CM He International Journal of Biomathematics 10 (04), 1750050, 2017 | 61 | 2017 |
| Recent advances in conotoxin classification by using machine learning methods FY Dao, H Yang, ZD Su, W Yang, Y Wu, H Ding, W Chen, H Tang, H Lin Molecules 22 (7), 1057, 2017 | 59 | 2017 |
| Identify and analysis crotonylation sites in histone by using support vector machines WR Qiu, BQ Sun, H Tang, J Huang, H Lin Artificial intelligence in medicine 83, 75-81, 2017 | 57 | 2017 |
