Stanley, Kevin2020-10-092020-10-092020-092020-10-09Septemberhttp://hdl.handle.net/10388/13096Inference of microbial interaction types allows us to understand the growth and development of microbial life forms found on earth. Numerous methods have been proposed to infer the interaction type(s) of microbes in a microbial communities using a population dynamics model. However, due to dynamic behaviour of microbial communities, these methods can result in erroneous inferences. A method proposed by Xiao et al. in 2017 models the dynamic behaviour of microbial community using sample abundance data overcomes many of these issues, but suffers from a high failure rate of inference, lower confidence on inferred interactions and slower execution speed than the existing algorithms. In this thesis, we propose an improved and more efficient and effective approach to infer the microbial interaction types of larger microbial communities (N>10). Our findings demonstrate that our approach is faster, more fault tolerant, more scalable than the state of the art from 2017, and it has the ability to infer microbial interactions with increased confidence.application/pdfmicrobial interactionsunsupervisedThesis2020-10-09