Computational Biology for Human Development: Overcoming Imperfect Data
A growing awareness of developmental impacts on lifelong health has increased interest in improving our understanding of human development at the molecular level. However, the information needed to interpret developmental genomic data in a useful way is typically imperfect, incomplete, or lacking in context. In this talk, we will discuss these barriers and our experience addressing them using computational methods.
Adding context-specific functional annotation has improved our ability to interpret developmental data sets. Pooling imperfect gene-disease data across related disease processes has helped us link developmental processes to health outcomes. A new-anomaly detection paradigm for the analysis of expression data sets facilitates interpretation of individual samples. Our recent results in developmental bioinformatics illustrate how domain expertise, contextual awareness, and scaling up can help us overcome the limits of the data.
Donna Slonim is Associate Professor of Computer Science at Tufts University, with secondary appointments in the School of Medicine and with the Genetics program at the Sackler School of Graduate Biomedical Sciences. She holds a Ph.D. in Computer Science from the Massachusetts Institute of Technology (1996), an M.S. in Computer Science from the University of California at Berkeley (1991), and a B.S. degree from Yale University (1990). Her research focuses on applying a computational mindset to molecular biology and biomedical data, with the aim of advancing our understanding of human health and disease.