| GNE: A deep learning framework for gene network inference by aggregating biological information K KC, R Li, F Cui, Q Yu, A Haake BMC Systems Biology, 2019 | 36 | 2019 |
| Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches PN Yin, K KC, S Wei, Q Yu, R Li, AR Haake, H Miyamoto, F Cui BMC medical informatics and decision making 20 (1), 1-11, 2020 | 9 | 2020 |
| Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks K KC, R Li, F Cui, A Haake IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021 | 7* | 2021 |
| Joint Inference for Neural Network Depth and Dropout Regularization KC Kishan, R Li, M Gilany Neural Information Processing Systems, 2021 | 1* | 2021 |
| Interpretable Structured Learning with Sparse Gated Sequence Encoder for Protein-Protein Interaction Prediction K KC, F Cui, AR Haake, R Li International Conference on Pattern Recognition (ICPR), 7126-7133, 2021 | 1 | 2021 |
| openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer K K C, Z Tan, L Chen, M Jin, E Han, A Stolcke, C Lee arXiv preprint arXiv:2202.12349, 2022 | | 2022 |
| Scalable Probabilistic Model Selection for Network Representation Learning in Biological Network Inference K K C Rochester Institute of Technology, 2022 | | 2022 |
| Machine learning predicts nucleosome binding modes of transcription factors K KC, SK Subramanya, R Li, F Cui BMC Bioinformatics 22 (1), 1471-2105, 2021 | | 2021 |
| (Poster) Learning topology-preserving embedding for gene interaction networks K KC, R Li, F Cui, AR Haake 17th European Conference on Computational Biology (Poster), 2018 | | 2018 |
Rui LiRochester Institute of TechnologyVerified email at rit.edu
