Narrative: Missing data is a common problem in the dynamic biological networks (a biological network is a set of genes or proteins that interact with each other to control a specific cell function.) For example, in order to construct a dynamic network of a cancer patient, data from this patient should be collected at multiple time points. However, the patient may not be available at some of those time points. One way to study dynamic networks with such missing data is to restrict the analysis to only the time points at which data is available. However, this results in missing key temporal changes. Constructing the topology of networks at such missing time points is of utmost importance to have a holistic view on how a human disease evolves. In this study, we will develop a novel computational method, utilizing mathematical modeling and machine learning, that constructs the dynamic biological networks at the missing time points. Using this method, we will study how different cancer types changes the human biological regulatory networks through time.
Programming skills from MATH 260 (Computational Mathematics), MATH 376 (Numerical Analysis) or COSC 102 (Introduction to Computing II).
Having taken COSC 301 (Operating Systems) is preferred.
Number of Student Researchers
Applications open on 01/03/2020 and close on 03/11/2020