معرفی
Wanwen Zeng is a Postdoctoral Scholar in the Department of Statistics at Stanford University, advised by Wing H Wong. Their research focuses on computational biology, integrating genomics, epigenomics, and machine learning to address problems in genetics and drug discovery.
Key research interests include gene regulatory networks, epigenetic prediction using deep learning (e.g., transformers), and developing bioinformatics tools for analyzing single-cell data. Their work spans databases like HiChIPdb (regulatory interactions) and SilencerDB, alongside methodological advancements such as CREATE and scGraph.
Recent publications highlight applications of graph neural networks in drug discovery (DeepDrug), causal modeling of genotype-phenotype relationships, and improving polygenic predictions through epigenomic features. This reflects a strong emphasis on bridging computational methods with biological systems.
No scientific awards are listed. Advising and grants sections remain unspecified in available data. Their contributions are primarily technical, centered on advancing genomic data analysis frameworks and databases.
