Evolutionary Data Science in Pathogen Phylodynamics

Evolutionary Data Science in Pathogen Phylodynamics (EVO-PATH) is an interdisciplinary field that combines principles of computational biology, epidemiology, immunology, and data science to analyze and interpret the phylogenetic trees of pathogens. We focus on understanding the evolutionary processes that influence the genetic diversity and adaptation of pathogens, amd integrating clinical and epidemiological data to study the spread and dynamics of infectious diseases. Advanced computational methods are employed to decipher the complex interactions between host immune responses, viral evolution, and the environmental factors driving these genetic features. The goal is to gain insights into pathogen evolution and transmission, and to inform public health strategies for disease control and prevention. Three specific research directions are as follows:

• Firstly, it dedicates to inferring epidemiological and evolutionary processes of emerging viruses through sequence and phylogenetic tree analyses, providing insights into virus evolution and transmission.
• Secondly, the lab excels in detecting unknown pathogens by implementing a next-generation sequencing (NGS) data analysis pipeline, enhancing our ability to identify and respond to novel viral threats or other pathogens.
• Lastly, it pioneers in using deep learning to uncover novel genetic signatures, developing and applying advanced deep learning models to decipher complex genetic data, thereby contributing to the understanding of pathogen evolution and the development of new diagnostic tools.