Dr. Fertig advances a new predictive medicine paradigm for oncology by converging systems biology with translational technology development. Her wet lab develops time course models of therapeutic resistance and single cell technology development for analysis of clinical biospecimens. Her computational methods blend mathematical modeling and artificial intelligence to determine the biomarkers and molecular mechanisms of therapeutic resistance and disease progression from multi-platform genomics data. These techniques have broad applicability to the analysis of clinical biospecimens, developmental biology, and neuroscience.
Dr. Fertig is a Professor of Oncology and Division and Associate Cancer Center Director in Quantitative Sciences, co-Director of the Convergence Institute, and co-Director of the Single-Cell Training and Analysis Center at Johns Hopkins University. She has secondary appointments in Biomedical Engineering and Applied Mathematics and Statistics, affiliations in the Institute of Computational Medicine, Center for Computational Genomics, Machine Learning, Mathematical Institute for Data Science, and the Center for Computational Biology and is a Daniel Nathans Scientific Innovator. Prior to entering the field of computational cancer biology, Dr Fertig was a NASA research fellow in numerical weather prediction. Dr. Fertig's research is featured in over numerous peer-reviewed publications, R/Bioconductor packages, and competitive funding portfolio as PI and co-I. Notably, she led the team that won the HPN-DREAM8 algorithm to predict phospho-proteomic trajectories from therapeutic response in cancer cells and was elected to the College of Fellows American Institute for Medical and Biomedical Engineering (AIMBE) in 2022. She serves on the editorial boards of the pre-eminent computational biology journals PLoS Computational Biology, Cell Systems, ImmunoInformatics, eLife, and Cancer Research Communications, and as a steering committee member for the NCI Informatics Technology for Cancer Research Consortium.