Research

魏樂義 Wei Leyi

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 Biography

Dr. Leyi Wei graduated from Xiamen University, 2016. His research interests include artificial intelligence and bioinformatics. He has published 100+ peer-reviewed papers, receiving 6000+ citations in Google Scholar with h-index=41. His work has been recognized through the reception of awards, including “Highly Cited Researcher" in Cross-Field (Released by Clarivate Analytics, 2021), Elsevier Highly Cited Chiese Researcher (2021), ACM SIGBIO Rising Star Award (2021), and many others. In addition, He has rich experience, serving as Associate Editor and the Editorial Board member for a number of well-known journals, such as Frontiers in Genetics, Methods, BMC Genomics, and Current Bioinformatics, etc.

 Education and Experiences

Educational background
2013-2016, School of Software, Xiamen University, Ph.D.
2010-2013, Department of Computer Science, School of Information Science and Engineering, Xiamen University, M.Sc.
2006-2010, School of Mathematical Sciences, Xiamen University, B.Sc.

 Research Interests

• Artificial intelligence; Bioinformatics

 Selected Publications

1. Jiang, Y., Wang, R., Feng, J., Jin, J., Liang, S., Li, Z., ... & Wei, L.* (2023). Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction. Advanced Science, Accepted.
2. Wang, R., Jiang, Y., Jin, J., Yin, C., Yu, H., Wang, F., ... & Wei, L.* (2023). DeepBIO: An automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation, and visualization analysis. Nucleic Acids Research.gkad055.
3. Jin, J., Yu, Y., Wang, R., Zeng, X., Pang, C., Jiang, Y., ... & Wei, L.* (2022). iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations. Genome biology, 23(1), 1-23.
4. Dai, C., Jiang, Y., Yin, C., Su, R., Zeng, X., Zou, Q., ... & Wei, L.* (2022). scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods. Nucleic Acids Research, 50(9), 4877-4899.
5. He, W., Jiang, Y., Jin, J., Li, Z., Zhao, J., Manavalan, B., ... & Wei, L.* (2022). Accelerating bioactive peptide discovery via mutual information-based meta-learning. Briefings in Bioinformatics, 23(1), bbab499.