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Xinming Tu
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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
462022
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
292020
What should data science education do with large language models
X Tu, J Zou, WJ Su, L Zhang
arXiv preprint arXiv:2307.02792, 2023
92023
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
92022
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
42021
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
22023
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
22017
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
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Articles 1–9