| Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier L Wei, P Xing, J Zeng, JX Chen, R Su, F Guo Artificial Intelligence in Medicine 83, 67-74, 2017 | 235 | 2017 |
| Identification of drug-side effect association via multiple information integration with centered kernel alignment Y Ding, J Tang, F Guo Neurocomputing 325, 211-224, 2019 | 195 | 2019 |
| Identification of drug-target interactions via multiple information integration Y Ding, J Tang, F Guo Information Sciences 418, 546-560, 2017 | 178 | 2017 |
| Application of machine learning in microbiology K Qu, F Guo, X Liu, Y Lin, Q Zou Frontiers in microbiology 10, 451710, 2019 | 173 | 2019 |
| MRMD2. 0: a python tool for machine learning with feature ranking and reduction S He, F Guo, Q Zou Current Bioinformatics 15 (10), 1213-1221, 2020 | 160 | 2020 |
| Predicting protein-protein interactions via multivariate mutual information of protein sequences Y Ding, J Tang, F Guo BMC bioinformatics 17, 1-13, 2016 | 152 | 2016 |
| Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC Y Shen, J Tang, F Guo Journal of Theoretical Biology 462, 230-239, 2019 | 151 | 2019 |
| Identification of drug–target interactions via dual laplacian regularized least squares with multiple kernel fusion Y Ding, J Tang, F Guo Knowledge-Based Systems 204, 106254, 2020 | 126 | 2020 |
| MK-FSVM-SVDD: a multiple kernel-based fuzzy SVM model for predicting DNA-binding proteins via support vector data description Y Zou, H Wu, X Guo, L Peng, Y Ding, J Tang, F Guo Current Bioinformatics 16 (2), 274-283, 2021 | 117 | 2021 |
| Identification of membrane protein types via multivariate information fusion with Hilbert–Schmidt independence criterion H Wang, Y Ding, J Tang, F Guo Neurocomputing 383, 257-269, 2020 | 108 | 2020 |
| Identification of drug–target interactions via fuzzy bipartite local model Y Ding, J Tang, F Guo Neural Computing and Applications 32 (14), 10303-10319, 2020 | 89 | 2020 |
| Identification of protein–protein interactions via a novel matrix-based sequence representation model with amino acid contact information Y Ding, J Tang, F Guo International journal of molecular sciences 17 (10), 1623, 2016 | 89 | 2016 |
| Identification of drug-side effect association via semisupervised model and multiple kernel learning Y Ding, J Tang, F Guo IEEE journal of biomedical and health informatics 23 (6), 2619-2632, 2018 | 84 | 2018 |
| Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine P Xing, R Su, F Guo, L Wei Scientific reports 7 (1), 46757, 2017 | 78 | 2017 |
| Identification of protein–ligand binding sites by sequence information and ensemble classifier Y Ding, J Tang, F Guo Journal of Chemical Information and Modeling 57 (12), 3149-3161, 2017 | 77 | 2017 |
| DeepAVP: a dual-channel deep neural network for identifying variable-length antiviral peptides J Li, Y Pu, J Tang, Q Zou, F Guo IEEE journal of biomedical and health informatics 24 (10), 3012-3019, 2020 | 75 | 2020 |
| MDA-SKF: similarity kernel fusion for accurately discovering miRNA-disease association L Jiang, Y Ding, J Tang, F Guo Frontiers in Genetics 9, 618, 2018 | 75 | 2018 |
| AOPs-SVM: a sequence-based classifier of antioxidant proteins using a support vector machine C Meng, S Jin, L Wang, F Guo, Q Zou Frontiers in Bioengineering and Biotechnology 7, 224, 2019 | 72 | 2019 |
| Improved detection of DNA-binding proteins via compression technology on PSSM information Y Wang, Y Ding, F Guo, L Wei, J Tang PloS one 12 (9), e0185587, 2017 | 70 | 2017 |
| Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment H Wang, J Tang, Y Ding, F Guo Briefings in Bioinformatics 22 (5), bbaa409, 2021 | 64 | 2021 |
Jijun TangUniversity of South CarolinaVerified email at cse.sc.edu
