Shula Shazman, Researcher and Lecturer at The Open University of Israel, Israel
Title : Predicting the Ability of Intermittent Fasting to Improve Health in Type 2 Diabetes
Intermittent fasting (IF) is a cyclical combination of eating and fasting periods. The different types of IF can be effective inter alia in reducing metabolic disorders and age-related diseases such as type 2 diabetes (T2D). The effectiveness in improving health using intermittent fasting regimen is manifested by bringing about changes in metabolic parameters associated with T2D. For the purposes of this research, the health outcomes of interest are reduction in fasting glucose and insulin. However, questions remain about the effects of the different types of IF regimens as a function of the age at which fasting begins, gender and severity of T2D. Here I will describe several machine learning approaches to provide a recommendation system which reveals a set of rules that can assist selecting a successful IF intervention for a personal case. In addition, I will discuss the question: Can we predict the optimal IF intervention for a prediabetes patient ?
Shula Shazman received her B.Sc. in Computer Sciences from the Technion, Israel in 1993. In 2011 she has finished her M.Sc. + Ph.D. (direct track) in Biological Sciences, In the faculty of biology, at the Technion, Israel. The title of thesis is Computational approaches for characterizing Protein- Nucleic-Acid Binding. Supervisor: Prof. Yael Mandel-Gutfreund. During 2011 – 2015 she has been a postdoctoral fellow in the Department of Biochemistry & Molecular Biophysics, Columbia University, New-York, USA. Supervisor: Prof. Barry Honig. Currently Shula works at the Department of Mathematics and Computer Science, The Open University of Israel. Shula does research in Bioinformatics. Their current projects are 'Disordered proteins' and 'Intermittent Fasting as a tool to treat Type 2 Diabetes'.