Biography NIH Biosketch PubMed Publications Departmental Website Research Focus Obesity is a world-wide problem, and two-thirds of the population in the United States is currently considered to be either overweight or obese. Obesity increases the risk for multiple different types of cancer. In particular, obesity significantly increases the risk for postmenopausal breast cancer. Obesity also …
Biography NIH Biosketch PubMed Publications Departmental Website Research Focus How does a newly formed stem or progenitor cell “know” it had reached its full differentiation capacity, and should assume its mature function? What part of the signal towards terminal differentiation comes from the cell’s interactions with its surrounding environment, and what is encoded in the …
Biography NIH Biosketch PubMed Publications Research Focus Dr. Ehrenthal’s research uses a life course framework to investigate the determinants of health outcomes and health disparities of populations. She focuses on three intersecting areas of women’s and children’s health: perinatal outcomes, women’s cardiovascular disease, and child health. The goal of her work is to expand the …
Biography NIH Biosketch PubMed Publications Department Website Research Focus How cells respond to information from their surroundings and use this information to make decisions. The Kreeger lab utilizes systems biology and tissue engineering to analyze this question in the context of women’s health, particularly breast cancer, ovarian cancer, and endometriosis. We utilize an iterative approach, …
Biography NIH Biosketch PubMed Publications Department Website Research Focus Our laboratory is focused on understanding how what, when, and how much we eat can regulate metabolic health and aging. We discovered that low protein diets promote metabolic health and lifespan – improving blood sugar control and reducing adiposity – in humans and mice, and we …
Biography NIH Biosketch PubMed Publications Departmental Website Research Focus Irene’s research interests lies in data mining, artificial intelligence, machine learning, probabilistic methods, dynamical models, inductive logic programming, and statistical relational learning with applications to biological and medical data. She is particularly interested in the integration and analysis of clinical, genomics, transcriptomics, proteomics, immunome, metagenomics, metabolomics, …