Title: Application of Linear Genetic Programming and Metabolomics in Alzheimer’s Disease
Group: Chengyuan Sha
Course: CISC 499
Description:Alzheimer’s disease (AD) is a progressive neurodegenerative disease. There are many people in Canada with this disease because it cannot be diagnosed effectively in the early stage. Metabolomics is the study of low-molecular-weight molecules. These metabolites are a result of complex biological processes, and thus are potential candidates to reflect disease.
Linear Genetic Programming (LGP) is a paradigm of genetic programming that uses a representation of linearly sequenced instructions in automatically generated programs. The term “linear” refers specifically to the structure of the program and does not limit the type of problems that LGP can be used to solve. In this project, I developed a python implementation of LGP with scikit-learn compatible API. I applied this evolutionary learning approach on plasma metabolites data and discovered five key metabolic biomarkers and their interactions. The results demonstrate a disturbance in fatty acid and amino acid metabolism, and the glutamate-glutamine cycle.