An evolutionary learning and network approach to identifying key metabolites for osteoarthritis.

• Published in: PLOS Computational Biology
• Main Authors: Ting Hu, Karoliina Oksanen, Weidong Zhang, Ed Randell, Andrew Furey, Guang Sun, Guangju Zhai
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2018
• Subjects: Biology (General); QH301-705.5; Newfoundland
• Online Access: https://doi.org/10.1371/journal.pcbi.1005986; https://doaj.org/article/61cf9fa11f5547be8c6e6431103b8f8c

Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogenesis of complex diseases. Most conventional metabolomics analysis methods exam one metabolite at a time and may overlook the synergistic effect of combining multiple metabolites. In this article, we proposed a new bioinformatics framework that infers the non-linear synergy among multiple metabolites using a symbolic model and subsequently, identify key metabolites using network analysis. Such a symbolic model is able to represent a complex non-linear relationship among a set of metabolites associated with osteoarthritis (OA) and is automatically learned using an evolutionary algorithm. Applied to the Newfoundland Osteoarthritis Study (NFOAS) dataset, our methodology was able to identify nine key metabolites including some known osteoarthritis-associated metabolites and some novel metabolic markers that have never been reported before. The results demonstrate the effectiveness of our methodology and more importantly, with further investigations, propose new hypotheses that can help better understand the OA disease.