Predicting youth diabetes risk using NHANES data and machine learning N Vangeepuram, B Liu, P Chiu, L Wang, G Pandey Scientific reports 11 (1), 11212, 2021 | 16 | 2021 |
Integrating multimodal data through interpretable heterogeneous ensembles YC Li, L Wang, JN Law, TM Murali, G Pandey Bioinformatics advances 2 (1), vbac065, 2022 | 14* | 2022 |
Large-scale protein function prediction using heterogeneous ensembles L Wang, J Law, SD Kale, TM Murali, G Pandey F1000Research 7, 2018 | 14 | 2018 |
Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data L Wang, M Maletic-Savatic, Z Liu Nature Communications 13 (1), 6912, 2022 | 8* | 2022 |
Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss C Liu, L Wang, Z Liu BMC bioinformatics 24 (1), 5, 2023 | 4 | 2023 |
Quantification and visualization of cis-regulatory dynamics in single-cell multi-omics data with TREASMO C Liu, L Wang, Z Liu NAR Genomics and Bioinformatics 6 (1), lqae007, 2024 | 1* | 2024 |
AI-MARRVEL—A Knowledge-Driven AI System for Diagnosing Mendelian Disorders D Mao, C Liu, L Wang, R AI-Ouran, C Deisseroth, S Pasupuleti, SY Kim, ... NEJM AI 1 (5), AIoa2300009, 2024 | | 2024 |
Unravelling spatial gene associations with SEAGAL: a Python package for spatial transcriptomics data analysis and visualization L Wang, C Liu, Y Gao, XHF Zhang, Z Liu Bioinformatics 39 (7), 2023 | | 2023 |
Accurate cell type deconvolution in spatial transcriptomics using a batch effect-free strategy L Wang, L Wu, C Liu, W Wang, XHF Zhang, Z Liu bioRxiv, 2022.12. 15.520612, 2022 | | 2022 |
Latent factor in Brain RNA-seq studies reflects cell type and clinical heterogeneity R Al-Ouran, C Liu, L Wang, YW Wan, X Li, A Milosavljevic, JM Shulman, ... bioRxiv, 2022.11. 13.516360, 2022 | | 2022 |