Robust microbial markers for non-invasive inflammatory bowel disease identification

Wingfield, B, Coleman, S, McGinnity, TM ORCID: https://orcid.org/0000-0002-9897-4748 and Bjourson, A, 2018. Robust microbial markers for non-invasive inflammatory bowel disease identification. IEEE/ACM Transactions on Computational Biology and Bioinformatics. ISSN 1545-5963

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Abstract

Inflammatory Bowel Disease (IBD) is an umbrella term for a group of inflammatory diseases of the human gastrointestinal tract, including Crohn’s Disease (CD) and ulcerative colitis (UC). Changes to the intestinal microbiome, the community of micro-organisms that resides in the human gut, have been shown to contribute to the pathogenesis of IBD. IBD diagnosis is often delayed due its non-specific symptoms (e.g. abdominal pain) and an invasive colonoscopy is required for confirmation. Delayed diagnosis is linked to poor growth in children and worse treatment outcomes. Microbial communities are extremely complex and feature selection algorithms are often applied to identify key bacterial groups that drive disease. It has been shown that aggregating Ensemble Feature Selection (EFS) can be used to improve the robustness of feature selection algorithms. The robustness of a feature selector is defined as the variation of feature selector output caused by small changes to the dataset. Typical feature selection algorithms can be used to help build simpler, faster, and easier to understand models - but suffer from poor robustness. Having confidence in the output of a feature selector algorithm is key for enabling knowledge discovery from complex biological datasets. In this work we apply a two-step filter and an EFS process to generate robust feature subsets that can non-invasively predict IBD subtypes from high-resolution microbiome data. The predictive power of the robust feature subsets is the highest reported in literature to date. Furthermore, we identify five biologically plausible bacterial species that have not previously been implicated in IBD aetiology.

Item Type:	Journal article
Publication Title:	IEEE/ACM Transactions on Computational Biology and Bioinformatics
Creators:	Wingfield, B., Coleman, S., McGinnity, T.M. and Bjourson, A.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	30 April 2018
ISSN:	1545-5963
Identifiers:	Number Type 10.1109/TCBB.2018.2831212 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	11 May 2018 10:00
Last Modified:	30 Jul 2018 11:37
URI:	https://irep.ntu.ac.uk/id/eprint/33536

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