This research work focuses on developing tensor-based models for analyzing high-order biological data such as spatial and temporal genomic data and multi-relations in biological networks.
Publications:
Deciphering High-order Structures in Spatial Transcriptomes with Graph-guided Tucker Decomposition Proceedings Forthcoming
To appear In the Proceedings of International Conference on Intelligent Systems for Molecular Biology (ISMB) 2024, Forthcoming.
GNTD: Reconstructing Spatial Transcriptomes with Graph-guided Neural Tensor Decomposition Informed by Spatial and Functional Relations Journal Article
In: Nature Communications, vol. 14, no. 8276, 2023.
Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion Journal Article
In: PLoS computational biology, vol. 17, no. 4, pp. e1008218, 2021.
Scalable Label Propagation for Multi-relational Learning on the Tensor Product of Graphs Journal Article
In: IEEE Transactions on Knowledge and Data Engineering, 2021.
Learning a Low-rank Tensor of Pharmacogenomic Multi-relations from Biomedical Networks Proceedings
IEEE International Conference on Data Mining 2019.