| Review of single-cell rna-seq data clustering for cell type identification and characterization S Zhang*, X Li, J Lin, Q Lin, KC Wong RNA 29 (5), 517-530, 2023 | 61 | 2023 |
| CRISPR‐Net: A Recurrent Convolutional Network Quantifies CRISPR Off‐Target Activities with Mismatches and Indels J Lin, Z Zhang, S Zhang, J Chen, KC Wong* Advanced Science, 2020 | 53 | 2020 |
| Synergizing CRISPR/Cas9 off-target predictions for ensemble insights and practical applications S Zhang, X Li, Q Lin, KC Wong* Bioinformatics 35 (7), 1108-1115, 2019 | 36 | 2019 |
| Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA Z Yu, Y Su, Y Lu, Y Yang, F Wang, S Zhang, Y Chang, KC Wong*, X Li* Nature Communications 14 (1), 400, 2023 | 29 | 2023 |
| Nature-inspired multiobjective epistasis elucidation from genome-wide association studies X Li, S Zhang, KC Wong* IEEE/ACM transactions on computational biology and bioinformatics 17 (1 …, 2018 | 29 | 2018 |
| Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning X Li, S Zhang, KC Wong* Bioinformatics 35 (16), 2809-2817, 2019 | 28 | 2019 |
| A novel method based on FTS with both GA-FCM and multifactor BPNN for stock forecasting W Zhang, S Zhang, S Zhang*, D Yu, NN Huang Soft Computing 23 (16), 6979-6994, 2019 | 28 | 2019 |
| A multi-factor and high-order stock forecast model based on Type-2 FTS using cuckoo search and self-adaptive harmony search W Zhang*, S Zhang, S Zhang, D Yu, NN Huang Neurocomputing 240, 13-24, 2017 | 25 | 2017 |
| Early cancer detection from multianalyte blood test results KC Wong*, J Chen, J Zhang, J Lin, S Yan, S Zhang, X Li, C Liang, C Peng, ... Iscience 15, 332-341, 2019 | 19 | 2019 |
| Identification of pan-cancer Ras pathway activation with deep learning X Li, S Li, Y Wang, S Zhang, KC Wong* Briefings in Bioinformatics 22 (4), bbaa258, 2021 | 16 | 2021 |
| Uncovering the Key Dimensions of High-Throughput Biomolecular Data using Deep Learning S Zhang, X Li, Q Lin, J Lin, KC Wong* Nucleic Acids Research 48 (10), e56-e56, 2020 | 14 | 2020 |
| Feature Selection and Feature Extraction: Highlights HM Wong, X Chen, HH Tam, J Lin, S Zhang, S Yan, X Li, KC Wong* 2021 5th International Conference on Intelligent Systems, Metaheuristics …, 2021 | 12 | 2021 |
| Two-factor high-order fuzzy-trend FTS model based on BSO-FCM and improved KA for TAIEX stock forecasting W Zhang, S Zhang, S Zhang* Nonlinear Dynamics 94, 1429-1446, 2018 | 12 | 2018 |
| High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning X Li, S Li, L Huang, S Zhang, K Wong Briefings in Bioinformatics 23 (1), bbab368, 2022 | 10 | 2022 |
| Deep embedded clustering with multiple objectives on scRNA-seq data X Li, S Zhang, KC Wong Briefings in Bioinformatics 22 (5), bbab090, 2021 | 10 | 2021 |
| A novel method for MCDM and evaluation of manufacturing services using collaborative filtering and IVIF theory W Zhang, S Zhang*, S Zhang, D Yu Journal of Algorithms & Computational Technology 10 (1), 40-51, 2016 | 10 | 2016 |
| Elucidating transcriptomic profiles from single-cell RNA sequencing data using nature-inspired compressed sensing Z Yu, C Bian, G Liu, S Zhang, KC Wong, X Li Briefings in Bioinformatics 22 (5), bbab125, 2021 | 9 | 2021 |
| Multiobjective genome-wide RNA-binding event identification from CLIP-seq data X Li, S Zhang, KC Wong* IEEE Transactions on Cybernetics 51 (12), 5811-5824, 2020 | 9 | 2020 |
| Nature-inspired compressed sensing for transcriptomic profiling from random composite measurements S Zhang, X Li, Q Lin, KC Wong* IEEE transactions on cybernetics 51 (9), 4476-4487, 2019 | 7 | 2019 |
| Efficient generation of paired single-cell multiomics profiles by deep learning M Lan, S Zhang*, L Gao* Advanced Science, 2023 | 6 | 2023 |
Ka-Chun WongStanford's top 2% most highly cited scientists (v5,v6,v7)Email yang diverifikasi di cs.toronto.edu