| iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition Z Liu, X Xiao, WR Qiu, KC Chou Analytical biochemistry 474, 69-77, 2015 | 286 | 2015 |
| pRNAm-PC: Predicting N6-methyladenosine sites in RNA sequences via physical–chemical properties Z Liu, X Xiao, DJ Yu, J Jia, WR Qiu, KC Chou Analytical biochemistry 497, 60-67, 2016 | 285 | 2016 |
| iPTM-mLys: identifying multiple lysine PTM sites and their different types WR Qiu, BQ Sun, X Xiao, ZC Xu, KC Chou Bioinformatics 32 (20), 3116-3123, 2016 | 274 | 2016 |
| iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components WR Qiu, X Xiao, KC Chou International journal of molecular sciences 15 (2), 1746-1766, 2014 | 266 | 2014 |
| MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction D Wang, S Zeng, C Xu, W Qiu, Y Liang, T Joshi, D Xu Bioinformatics 33 (24), 3909-3916, 2017 | 252 | 2017 |
| iMethyl‐PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach WR Qiu, X Xiao, WZ Lin, KC Chou BioMed research international 2014 (1), 947416, 2014 | 217 | 2014 |
| iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition WR Qiu, SY Jiang, ZC Xu, X Xiao, KC Chou Oncotarget 8 (25), 41178, 2017 | 202 | 2017 |
| iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model WR Qiu, X Xiao, WZ Lin, KC Chou Journal of Biomolecular Structure and Dynamics 33 (8), 1731-1742, 2015 | 187 | 2015 |
| iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC WR Qiu, BQ Sun, X Xiao, ZC Xu, KC Chou Oncotarget 7 (28), 44310, 2016 | 184 | 2016 |
| iPhos‐PseEvo: identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory WR Qiu, BQ Sun, X Xiao, D Xu, KC Chou Molecular Informatics 36 (5-6), 1600010, 2017 | 173 | 2017 |
| iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier WR Qiu, BQ Sun, X Xiao, ZC Xu, JH Jia, KC Chou Genomics 110 (5), 239-246, 2018 | 166 | 2018 |
| iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier WR Qiu, X Xiao, ZC Xu, KC Chou Oncotarget 7 (32), 51270, 2016 | 157 | 2016 |
| iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC H Yang, WR Qiu, G Liu, FB Guo, W Chen, KC Chou, H Lin International journal of biological sciences 14 (8), 883, 2018 | 151 | 2018 |
| A generalized method for forecasting based on fuzzy time series W Qiu, X Liu, H Li Expert Systems with Applications 38 (8), 10446-10453, 2011 | 128 | 2011 |
| iRNA-2methyl: identify RNA 2'-O-methylation sites by incorporating sequence-coupled effects into general PseKNC and ensemble classifier WR Qiu, SY Jiang, BQ Sun, X Xiao, X Cheng, KC Chou Medicinal Chemistry 13 (8), 734-743, 2017 | 127 | 2017 |
| iPPI-PseAAC (CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC J Jia, X Li, W Qiu, X Xiao, KC Chou Journal of theoretical biology 460, 195-203, 2019 | 101 | 2019 |
| iPSW (2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition X Xiao, ZC Xu, WR Qiu, P Wang, HT Ge, KC Chou Genomics 111 (6), 1785-1793, 2019 | 90 | 2019 |
| iRNAD: a computational tool for identifying D modification sites in RNA sequence ZC Xu, PM Feng, H Yang, WR Qiu, W Chen, H Lin Bioinformatics 35 (23), 4922-4929, 2019 | 81 | 2019 |
| Similarity measurement between normal cloud models HL Li, CH Guo, WR Qiu Dianzi Xuebao(Acta Electronica Sinica) 39 (11), 2561-2567, 2011 | 61 | 2011 |
| 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 | 60 | 2017 |
