Jing Xu

Dr. Jing Xu is a research scientist with extensive experience in computational biology, bioinformatics, and machine learning-driven biomedical data analysis. Her research spans multiple interdisciplinary areas, including antimicrobial peptide (AMP) prediction and design, enzyme annotation, disease biomarker discovery, nanotoxicity prediction, and multi-omics data integration.
Dr. Xu has co-authored over 15 peer-reviewed publications in high-impact journals such as Briefings in Bioinformatics, NPJ Digital Medicine, Bioinformatics, Environmental Pollution, and Global Change Biology. Her notable contributions include the development of iAMPCN, a deep-learning framework for identifying antimicrobial peptides and their functional activities; eCAMI, a simultaneous classification and motif identification tool for enzyme annotation; and comprehensive benchmarking studies of machine learning methods for AMP prediction. She has also worked on novel approaches integrating metabolomics and machine learning for predicting nanotoxicity, and applied ensemble learning methods to uncover disease-associated genes and microbial adaptation mechanisms.