Multimodal single cell data integration challenge: results and lessons learned C Lance, MD Luecken, DB Burkhardt, R Cannoodt, P Rautenstrauch, ... BioRxiv, 2022.04. 11.487796, 2022 | 46 | 2022 |
Expectation pooling: an effective and interpretable pooling method for predicting DNA–protein binding X Luo, X Tu, Y Ding, G Gao, M Deng Bioinformatics 36 (5), 1405-1412, 2020 | 29 | 2020 |
What should data science education do with large language models X Tu, J Zou, WJ Su, L Zhang arXiv preprint arXiv:2307.02792, 2023 | 9 | 2023 |
Cross-Linked Unified Embedding for cross-modality representation learning X Tu, ZJ Cao, CR Xia, S Mostafavi, G Gao Advances in Neural Information Processing Systems 35, 15942-15955, 2022 | 9 | 2022 |
Spatial-linked alignment tool (SLAT) for aligning heterogenous slices CR Xia, ZJ Cao, XM Tu, G Gao Nature Communications 14 (1), 7236, 2023 | 4* | 2023 |
Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network JY Li, S Jin, XM Tu, Y Ding, G Gao Briefings in Bioinformatics 22 (6), bbab233, 2021 | 4 | 2021 |
Evaluation and optimization of sequence-based gene regulatory deep learning models AM Rafi, D Nogina, D Penzar, D Lee, D Lee, N Kim, S Kim, D Kim, Y Shin, ... bioRxiv, 2023 | 2 | 2023 |
An exact transformation for cnn kernel enables accurate sequence motif identification and leads to a potentially full probabilistic interpretation of cnn Y Ding, J Li, M Wang, X Tu, G Gao | 2 | 2017 |
A Supervised Contrastive Framework for Learning Disentangled Representations of Cell Perturbation Data X Tu, JC Hutter, ZJ Wang, T Kudo, A Regev, R Lopez bioRxiv, 2024.01. 05.574421, 2024 | | 2024 |