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      Improved Inference of Ecological Interaction Types

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      AZIZ-THESIS-2020.pdf (2.165Mb)
      Date
      2020-10-09
      Author
      Aziz, Syed Umair
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      Inference 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.
      Degree
      Master of Science (M.Sc.)
      Department
      Computer Science
      Program
      Computer Science
      Supervisor
      Stanley, Kevin
      Committee
      Kusalik, Tony; Siciliano, Steven; Mondal, Debajyoti; Peak, Derek
      Copyright Date
      September 2020
      URI
      http://hdl.handle.net/10388/13096
      Subject
      microbial interactions
      unsupervised
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