| Reversed graph embedding resolves complex single-cell trajectories X Qiu, Q Mao, Y Tang, L Wang, R Chawla, HA Pliner, C Trapnell Nature methods 14 (10), 979, 2017 | 3405 | 2017 |
| Reversed graph embedding resolves complex single-cell developmental trajectories X Qiu, Q Mao, Y Tang, L Wang, R Chawla, H Pliner, C Trapnell bioRxiv, 110668, 2017 | 3405* | 2017 |
| Mapping Vector Field of Single Cells X Qiu, Y Zhang, D Yang, S Hosseinzadeh, L Wang, R Yuan, S Xu, Y Ma, ... bioRxiv, 696724, 2019 | 295* | 2019 |
| Learning sparse svm for feature selection on very high dimensional datasets M Tan, L Wang, IW Tsang Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010 | 221 | 2010 |
| Towards ultrahigh dimensional feature selection for big data M Tan, IW Tsang, L Wang The Journal of Machine Learning Research 15 (1), 1371-1429, 2014 | 192 | 2014 |
| Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe X Qiu, A Rahimzamani, L Wang, B Ren, Q Mao, T Durham, ... Cell Systems, 2020 | 139 | 2020 |
| Semidefinite relaxations for best rank-1 tensor approximations J Nie, L Wang SIAM Journal on Matrix Analysis and Applications 35 (3), 1155-1179, 2014 | 131 | 2014 |
| Dimensionality Reduction Via Graph Structure Learning Q Mao, L Wang, S Goodison, Y Sun Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 109 | 2015 |
| Principal Graph and Structure Learning Based on Reversed Graph Embedding Q Mao, L Wang, I Tsang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 | 84 | 2016 |
| Wind farm layout optimization based on support vector regression guided genetic algorithm with consideration of participation among landowners X Ju, F Liu, L Wang, WJ Lee Energy Conversion and Management 196, 1267-1281, 2019 | 80 | 2019 |
| Riemannian pursuit for big matrix recovery M Tan, IW Tsang, L Wang, B Vandereycken, SJ Pan Proceedings of the 31st International Conference on Machine Learning (ICML …, 2014 | 76 | 2014 |
| Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment L Zhang, L Wang, J Gao, SL Risacher, J Yan, G Li, T Liu, D Zhu, ... Medical Image Analysis, 102082, 2021 | 68 | 2021 |
| Regularization Methods for SDP Relaxations in Large Scale Polynomial Optimization J Nie, L Wang arXiv preprint arXiv:0909.3551, 2009 | 62* | 2009 |
| Matching pursuit LASSO Part I: Sparse recovery over big dictionary M Tan, IW Tsang, L Wang Signal Processing, IEEE Transactions on 63 (3), 727-741, 2015 | 51 | 2015 |
| Sprod for de-noising spatially resolved transcriptomics data based on position and image information Y Wang, B Song, S Wang, M Chen, Y Xie, G Xiao, L Wang, T Wang Nature methods 19 (8), 950-958, 2022 | 48 | 2022 |
| Minimax sparse logistic regression for very high-dimensional feature selection M Tan, IW Tsang, L Wang Neural Networks and Learning Systems, IEEE Transactions on 24 (10), 1609-1622, 2013 | 48 | 2013 |
| Predicting brain structural network using functional connectivity L Zhang, L Wang, D Zhu, Alzheimer's Disease Neuroimaging Initiative Medical Image Analysis 79, 102463, 2022 | 46 | 2022 |
| Wind farm macro-siting optimization with insightful bi-criteria identification and relocation mechanism in genetic algorithm F Liu, X Ju, N Wang, L Wang, WJ Lee Energy Conversion and Management 217, 112964, 2020 | 43 | 2020 |
| Regularization methods for SDP relaxations in large-scale polynomial optimization J Nie, L Wang SIAM Journal on Optimization 22 (2), 408-428, 2012 | 42 | 2012 |
| Towards inferring causal gene regulatory networks from single cell expression measurements X Qiu, A Rahimzamani, L Wang, Q Mao, T Durham, JL McFaline-Figueroa, ... BioRxiv, 426981, 2018 | 37 | 2018 |
Ivor W. Tsang, IEEE FellowInaugural A*STAR AI Fellow and Director, Centre for Frontier AI Research, A*STAREmail verificata su cfar.a-star.edu.sg