36. H2GnnDTI: Hierarchical heterogeneous graph neural networks for drug target interaction prediction. Submitted
35. CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data. Submitted
34. XVGAE: Cross-view graph autoencoders for spatial domain identification by integrating gene expression, spatial locations with histological images. Submitted
33. ViewFormer: Multi-View Few-Shot Learning via View-Shot Attention. Submitted
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32. Gang Wen, Limin Li, MMOSurv: Meta-learning for few-shot survival analysis with multi-omics data. Bioinformatics 2024
31. Yin Guo, Xianni Xiao, Limin Li. Identifying differentially expressed genes in RNA Sequencing genes in RNA Sequencing data with small labelled samples. TCBB 2024
30. Gang Wen, Limin Li, FGCNSurv: dually fused graph convolutional network for multi-omics survival prediction. Bioinformatics 2023
29. Limin Li, Yameng Zhao, Huiran Li, Shuqin Zhang. BLTSA: pseudotime prediction for single cells by branched local tangent space alignment. Bioinformatics 2023
28. Menglan Cai, Anna Vesely, Xu Chen, Limin Li* and Jelle Goeman*. NetTDP: Permutation-Based True Discovery Proportions for Differential Co-expression Network Analysis. Briefings in Bioinformatics 2022.
27. Yameng Zhao, Yin Guo, Limin Li. cKBET: assessing goodness of batch effect correction for single-cell RNA-seq. Frontiers of Computer Science 2022
26 Zhengyue Zhang, Zheng Zhai, Limin Li*. Graph Refinement via Simultaneously Low-rank and Sparse Approximation. SIAM Journal of Scientific Computing.Vol. 44, Iss. 3 2022
25. Tianjiao Li, Xingming Zhao*, Limin Li*. Co-VAE: Drug-target binding affinity prediction by co-regularized variational autoencoders. IEEE transactions on Pattern Analysis and Machine Intelligence(TPAMI) 2021.
24. Menglan Cai, Canh Hao Nguyen, Hiroshi Mamitsuka, Limin Li*. XGSEA: CROSS-species Gene Set Enrichment Analysis via domain adaptation. Briefings in Bioinformatics 2021
23. Huiran Li, Yin Guo, Menglan Cai, Limin Li*. MicroRNA-disease association prediction by matrix tri-factorization. BMC Genomics 2020
22. Yin Guo, Huiran Li, Menglan Cai, Limin Li*. Integrative subspace clustering by common and specific decomposition for cancer subtype identification. BMC Medical Genomics 2019
21. Yunda Hao, Menglan Cai, Limin Li*. Drug repositioning via matrix completion with multi-view side information. IET Systems Biology 2019.
20. Limin Li, Menglan Cai, Cross-species Data Classification by Domain Adaptation via Discriminative Heterogeneous Maximum Mean Discrepancy. IEEE/ACM transactions on Computational Biology and Bioinformatics(TCBB) 2019
19.Menglan Cai, Limin Li*. rPCMP: robust p-value combination by multiple partitions with applications to ATAC-seq data. BMC Systems Biology. 2018
18.Limin Li, Zhenyue Zhang. Semi-supervised Domain Adaptation by Covariance Matching. IEEE transactions on Pattern Analysis and Machine Intelligence(TPAMI).2018
17. Limin Li, Xiao He and Karsten Borgwardt. Multi-target drug repositioning by bipartite block-wise sparse multi-task learning. BMC systems biology 2018
16. Menglan Cai, Limin Li*. Subtype identification from heterogeneous TCGA datasets on a genomic scale by multi-view learning with enhanced consensus. BMC Medical Genomics 2017
15. Xiao He, Limin Li, Damian Roqueiro and Karsten Borgwardt. Multi-view Spectral Clustering on Conflicting Views. ECML/PKDD 2017
14. Limin Li, Menglan Cai. Drug target prediction by multi-view low rank embedding. IEEE/ACM transactions on Computational Biology and Bioinformatics(TCBB). 2017
13. Xinzhang, Limin Li, Michael K Ng, Shuqin Zhang, Drug-target Interaction Prediction by Integrating Multiview Network Data. Computational biololgy and chemistry. Volumn 69, pages 185-193, 2017
12. Zhenyue Zhang, Zheng Zhai and Limin Li*. Uniform projection for multi-view learning. IEEE transactions on Pattern Analysis and Machine Intelligence(TPAMI). Volumn 39, Issue 8. 2017 (corresponding author)
2015 and Before
11. Limin Li, Shuqin Zhang. Orthogonal projection correction for confounders in biological data classification. International journal of data mining and bioinformatics. vol 13, No 2. 2015.
10. Limin Li, Kiyoko F Aoki-Kinoshita, Wai-Ki Ching,Hao Jiang. On Using Physico-Chemical Properties of Amino Acids in String Kernels for Protein Classification via Support Vector Machines. Journal of Systems Science and Complexity ,2015 28(2) 504-516
9. Limin Li. MPGraph: : Multi-view Penalized Graph Clustering for Predicting Drug-target Interactions. IET Systems Biology ,Vol8:2,pp67-73, 2014
8. Limin Li, Hao Jiang, Yushan Qiu, Wai-Ki Ching, Vassilios S Vassiliadis. Discovery of metabolic biomarkers: flux analysis and reaction-reaction network approach，BMC Systems Biology , 7:S13 2013
7. Limin Li, Barbara Rakitsch, Karsten Borgwardt. ccSVM: correcting Support Vector Machines for confounding factors in biological data classification. Bioinformatics 2011 Vol 27, issue 13: ppi343-i348 (ISMB Proceeding 2011)
6. Limin Li, Xiaobo Zhou, Wai-Ki Ching and Ping Wang. Predicting Enzyme Targets for Cancer Drugs by Profiling Human Metabolic Reactions in NCI-60 Cell lines. BMC Bioinformatics 2010,11:501.
5. Limin Li, Wai-Ki Ching, Yat-Ming Chan and Hiroshi Mamitsuka. On Network-Based Kernel Methods for Protein-Protein Interactions with Application in Protein Function Prediction. Journal of Systems Science and Complexity, 23(2010) 917-930.
4. Limin Li, Wai-Ki Ching, Taga Yamaguchi and Kiyoko F. Aoki-Kinoshita. Weighted q-gram method for Glycan Classification.BMC Bioinformatics 2010, 11(S1):S33.
3. Wai-Ki Ching, Limin Li*, Nam-Kiu Tsing, Alice S.Wong and Kwai-Wa Ching. A Weighted Local Least Squares Imputation Method for Missing Value Estimation in Microarray Gene Expression Data. International Journal of Data Mining and Bioinformatics, Vol. 4, No.3 pp. 331 - 347 (2010). (* Corresponding author)
2. Wai-Ki Ching, Limin Li, Yat-Ming Chan and Hiroshi Mamitsuka. A Study of Network-based Kernel Methods on Protein-Protein Interaction for Protein Functions Prediction. The International Symposium on Optimization and Systems Biology, (OSB 2009).
1. Limin Li, Motoki Shiga, Wai-Ki Ching and Hiroshi Mamitsuka. Genome-wide Function Annotation with Network Modularity-based Clustering Using Sequence Similarities and Microarray Expressions. Genome Informatics Vol.22 (2009): 95-120.
