Using computational intelligence for knowledge discovery from the human microbiome

Student thesis: Doctoral Thesis

Abstract

Subtle changes in microbial populations that inhabit different areas of the human body — known as microbiomes or microbiota — can contribute to disease development, and restoring these imbalances may provide a cure. Localised and systemic diseases such as Inflammatory Bowel Disease (IBD) and depression have been linked with alterations to microbiota across the human body. Our understanding of how both diseases develop contains significant gaps, and the microbiome — described by some as our “second genome” — offers a compelling new area for knowledge discovery. This thesis aimed to advance the field of microbiome research and is an account of the work conducted whilst investigating the human gut and oral microbiome for links with IBD and depression. In this thesis, a hybrid model and aggregating ensemble feature selection (EFS) approach are applied to microbiome census data gathered from subjects with IBD. Microbial ecology techniques are applied to identify alterations to the oral microbiome in depressed subjects, and a multimodal Computational Intelligence (CI) classification paradigm known as a Super Self-Organising Map (sSOM) is applied to predict depression from a saliva sample. Finally, a rough set characterisation approach was developed and applied to gut and oral microbiome census data in depressed subjects to avoid destructive data normalisation and to enable knowledge discovery. The outcomes from the development of the hybrid model and aggregating EFS approach include the accurate non-invasive prediction of IBD, and the identification of novel and robust alterations to the gut microbiome in an adult cohort of IBD patients. The result provides a potential alternative to invasive colonoscopy, improve the time to diagnosis and treatment of IBD, and delivers new insights into the aetiology of IBD. The investigation of the oral microbiome identified novel alterations in depressed subjects for the first time. The changes to the structure and composition of the oral microbiome were significant enough to enable the accurate prediction of depression from a saliva sample. The results contribute to the microbiome-gutbrain axis theory by associating alterations to the oral microbiome with depression for the first time, and offer an alternative to subjective criteria for diagnosing depression, which currently relies on patient self-report and clinical judgement. The rough set microbiome characterisation approach replicated existing results and identified previously undescribed alterations to the gut microbiome in depressed subjects. The results provide an alternative approach to destructive normalisation techniques that are often applied to microbiome census data (identifying an optimal approach is an open research question), and contribute to our understanding of the microbiome-gut-brain axis, which could lead to psychobiotic treatments of depression in the future.
Date of AwardMar 2019
Original languageEnglish
SponsorsDepartment for Employment and Learning
SupervisorMartin Mc Ginnity (Supervisor), Tony Bjourson (Supervisor) & Sonya Coleman (Supervisor)

Keywords

  • Personalised Medicine
  • Machine Learning
  • Micobiota

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