| Microbial dark matter: from discovery to applications Y Zha, H Chong, P Yang, K Ning Genomics, Proteomics and Bioinformatics 20 (5), 867-881, 2022 | 27 | 2022 |
| Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches Y Zha, H Chong, H Qiu, K Kang, Y Dun, Z Chen, X Cui, K Ning Genome medicine 14 (1), 43, 2022 | 17 | 2022 |
| Utilizing microbiome approaches to assist source tracking, treatment and prevention of COVID-19: Review and assessment M Han, Y Zha, H Chong, C Zhong, K Ning Computational and structural biotechnology journal 18, 3615-3622, 2020 | 7 | 2020 |
| Enabling technology for microbial source tracking based on transfer learning: from ontology-aware general knowledge to context-aware expert systems H Chong, Q Yu, Y Zha, G Xiong, N Wang, C Sun, S Wu, W Chen, K Ning Biorxiv, 2021.01. 29.428751, 2021 | 5* | 2021 |
| Reliable and interpretable mortality prediction with strong foresight in COVID-19 patients: an international study from China and Germany T Bai, X Zhu, X Zhou, D Grathwohl, P Yang, Y Zha, Y Jin, H Chong, Q Yu, ... Frontiers in Artificial Intelligence 4, 672050, 2021 | 4 | 2021 |
| EXPERT: transfer learning-enabled context-aware microbial community classification H Chong, Y Zha, Q Yu, M Cheng, G Xiong, N Wang, X Huang, S Huang, ... Briefings in Bioinformatics 23 (6), bbac396, 2022 | 3 | 2022 |
| Microbiome sample comparison and search: from pair-wise calculations to model-based matching Y Zha, H Chong, K Ning Frontiers in Microbiology 12, 642439, 2021 | 3 | 2021 |
| Tracing human life trajectory using gut microbial communities by context-aware deep learning H Zhang, H Chong, Q Yu, Y Zha, M Cheng, K Ning Briefings in Bioinformatics 24 (1), bbac629, 2023 | | 2023 |
| Meta-Prism 2.0: Enabling algorithm and web server for ultra-fast, memory-efficient, and accurate analysis among millions of microbial community samples K Kang, H Chong, K Ning GigaScience 11, giac073, 2022 | | 2022 |
| Meta-Prism 2.0: Enabling algorithm for ultra-fast, accurate and memory-efficient search among millions of microbial community samples K Kang, H Chong, K Ning bioRxiv, 2020.11. 17.387811, 2020 | | 2020 |
Kang NingSchool of Life Science and Technology, Huazhong University of Science and Technology在 hust.edu.cn 的电子邮件经过验证
