Microbial Association Network Inference from Chronic Wound NGS Metagenomic Data

The structures and dynamics of microbes (e.g. bacteria, bacteriophages) in chronic wounds are closely related to the wound clinic phenotypes and their healing process. Methods to infer the latent structure of the association networks between microbes from observed next-generation sequencing data are critical for a deeper understanding of putative microbe-microbe interactions and the outcomes. We propose to use data-driven methods including graphical models and machine learning approaches to predict associations as putative interactions between bacteria and bacteriophage, based on clinic metagenomic data. 

Affiliated People

Research interests: 

computational biology, data mining, applied machine learning, dynamical systems modeling

Marianne is a 2nd year Computing student in the College of Creative Studies (CCS) and a UCSB CSEP research intern. She has an interest in applying computational methods to biological problems and she is currently working on using dynamic Bayesian networks to infer dynamic gut and nares microbial interaction networks in prediabetes patients during stress.

Research interests: 

Data Mining, Network Science, Bioinformatics

Yuning received his B.Sc in Chemistry from Fudan University, Shanghai, China in 2016. He is a Ph.D. student in the program of Chemistry and Biochemistry and works in Chen Lab with a focus on data-driven methods to model complex chemical/biological systems.