Artificial Intelligence approaches for demystifying cellular phenotypes through semantic knowledge networks

Cells are the fundamental units of life. Single cell genomic technologies are revolutionizing our understanding of cellular phenotypes. Large single cell data consortia, including the NIH BRAIN Initiative and the Human BioMolecular Atlas Program (HuBMAP), have generated single cell atlas data from millions of cells/nuclei spanning multiple organs and biological systems. At the National Library of Medicine (NLM), we are building the NLM Cell Knowledge Network (http://cell-kn-mvp.org), a knowledgebase that focuses on representing the cell phenotypes and associated characteristics derived from single cell genomics data. It integrates data-driven information with knowledge from trustworthy reference ontologies, NCBI resources, and text mining efforts, resulting in a large-scale semantic knowledge network for innovative data mining and knowledge discovery.

This project consists of two main research components: 
i) developing novel computational methods for single cell and spatial transcriptomics analysis using machine learning and advanced statistics techniques, and 
ii) developing network analysis strategies for knowledge mining using cutting-edge artificial intelligence technologies. 

Students interested in one or both research components are encouraged to apply. The project team has interdisciplinary background, ranging from molecular biology, genetics, statistics, and computer science, providing a strong supporting system for students’ academic growth. Dr. Yun (Renee) Zhang is a tenure-track investigator at NLM and an alumnus of the University of Oxford