| Interpretable machine learning for discovery: Statistical challenges and opportunities GI Allen, L Gan, L Zheng Annual Review of Statistics and Its Application 11, 2023 | 91 | 2023 |
| Inference for interpretable machine learning: Fast, model-agnostic confidence intervals for feature importance L Gan, L Zheng, GI Allen arXiv preprint arXiv:2206.02088 834, 2022 | 14 | 2022 |
| Fast and interpretable consensus clustering via minipatch learning L Gan, GI Allen PLOS Computational Biology 18 (10), e1010577, 2022 | 11 | 2022 |
| Model-agnostic confidence intervals for feature importance: A fast and powerful approach using minipatch ensembles L Gan, L Zheng, GI Allen arXiv preprint arXiv:2206.02088, 2022 | 10 | 2022 |
| Correlation Imputation for single-cell RNA-seq L Gan, G Vinci, GI Allen Journal of Computational Biology 29 (5), 465-482, 2022 | 9 | 2022 |
| Correlation imputation in single cell RNA-seq using auxiliary information and ensemble learning L Gan, G Vinci, GI Allen Proceedings of the 11th ACM International Conference on Bioinformatics …, 2020 | 4 | 2020 |
| Are machine learning interpretations reliable? A stability study on global interpretations L Gan, TM Zikry, GI Allen arXiv preprint arXiv:2505.15728, 2025 | 1 | 2025 |
| Integrated DNase I hypersensitivity prediction using RNA-seq and unmatched public DNase-seq L Gan Johns Hopkins University, 2019 | | 2019 |
