| Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST Y Long, KS Ang, M Li, KLK Chong, R Sethi, C Zhong, H Xu, Z Ong, ... Nature Communications 14 (1), 1155, 2023 | 231 | 2023 |
| Unsupervised spatially embedded deep representation of spatial transcriptomics H Xu, H Fu, Y Long, KS Ang, R Sethi, K Chong, M Li, R Uddamvathanak, ... Genome Medicine 16 (1), 12, 2024 | 187 | 2024 |
| Predicting human microbe–drug associations via graph convolutional network with conditional random field Y Long, M Wu, CK Kwoh, J Luo, X Li Bioinformatics 36 (19), 4918-4927, 2020 | 118 | 2020 |
| NTSHMDA: prediction of human microbe-disease association based on random walk by integrating network topological similarity J Luo, Y Long IEEE/ACM transactions on computational biology and bioinformatics 17 (4 …, 2018 | 117 | 2018 |
| Pre-training graph neural networks for link prediction in biomedical networks Y Long, M Wu, Y Liu, Y Fang, CK Kwoh, J Chen, J Luo, X Li Bioinformatics 38 (8), 2254-2262, 2022 | 86 | 2022 |
| Predicting Human Microbe-Disease Associations via Graph Attention Networks with Inductive Matrix Completion Y Long, J Luo, Y Zhang, Y Xia Briefings in Bioinformatics, 2020 | 71 | 2020 |
| Ensembling graph attention networks for human microbe–drug association prediction Y Long, M Wu, Y Liu, CK Kwoh, J Luo, X Li Bioinformatics 36 (Supplement_2), i779-i786, 2020 | 66 | 2020 |
| WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network Y Long, J Luo BMC Bioinformatics, 2019 | 39 | 2019 |
| Graph contextualized attention network for predicting synthetic lethality in human cancers Y Long, M Wu, Y Liu, J Zheng, CK Kwoh, J Luo, X Li Bioinformatics 37 (16), 2432-2440, 2021 | 38 | 2021 |
| Association mining to identify microbe drug interactions based on heterogeneous network embedding representation Y Long, J Luo IEEE Journal of Biomedical and Health Informatics, 2020 | 38 | 2020 |
| Deep learning based DNA: RNA triplex forming potential prediction Y Zhang, Y Long, CK Kwoh BMC bioinformatics 21, 1-13, 2020 | 32 | 2020 |
| Deciphering spatial domains from spatial multi-omics with SpatialGlue Y Long, KS Ang, R Sethi, S Liao, Y Heng, L van Olst, S Ye, C Zhong, H Xu, ... Nature Methods, 1-10, 2024 | 28 | 2024 |
| Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism B Wang, J Luo, Y Liu, W Shi, Z Xiong, C Shen, Y Long Briefings in Bioinformatics, 2023 | 27 | 2023 |
| Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks X Shi, J Zhu, Y Long, C Liang Briefings in Bioinformatics, 2023 | 21 | 2023 |
| DL-CRISPR: a deep learning method for off-target activity prediction in CRISPR/Cas9 with data augmentation Y Zhang, Y Long, R Yin, CK Kwoh IEEE Access 8, 76610-76617, 2020 | 19 | 2020 |
| Heterogeneous graph attention networks for drug virus association prediction Y Long, Y Zhang, M Wu, S Peng, CK Kwoh, J Luo, X Li Methods 198, 11-18, 2022 | 17 | 2022 |
| Energy-efficient multi-focus image fusion based on neighbor distance and morphology C Liu, Y Long, J Mao Optik 127 (23), 11354-11363, 2016 | 14 | 2016 |
| Predicting Drugs for COVID-19/SARS-CoV-2 via Heterogeneous Graph Attention Networks Y Long, Y Zhang, M Wu, S Peng, CK Kwoh, J Luo, X Li IEEE International Conference on Bioinformatics and Biomedicine (BIBM2020), 2020 | 5 | 2020 |
| Interpretable multi-view attention network for drug-drug interaction prediction X Lin, Q Wen, S Yang, ZG Yu, Y Long, X Zeng 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2023 | 4 | 2023 |
| An effective image fusion algorithm based on grey relation of similarity and morphology C Liu, Y Long, J Mao, H Zhang, R Huang, Y Dai Journal of Ambient Intelligence and Humanized Computing 14 (11), 14859-14872, 2023 | 4 | 2023 |
Jiawei LuoProfessor of Computer Science, Hunan UniversityCorreu electrònic verificat a hnu.edu.cn
