P070 Machine learning approaches to identify IBD biomarkers from longitudinal microbiome data

Abstract

S171 Results: As a baseline, we used multiple ML models to predict disease type (UC, CD and non-IBD) from integrated multi-omics profiles. We analysed multiple ML techniques, including linear (e.g. linear mixed model), non-linear (e.g. Random Forest), time-series models (e.g. Rotation Forest) and deep learning models (e.g. long short-term memory network model). The authors identified the models which would allow flexibility to analyse the dynamic nature of the micro-biome and allow integration of the microbiome data with clinical patient data. The payoff of greater flexibility was a reduction in the model performance in terms of identifying specific features from the metagenomics that could be used as biomarkers. However, we were able to identify connections between microbial and host proteins relevant to IBD and were able to stratify these by the patient's metagenomic data. Conclusion: We have developed an integrated ml-based microbiome analysis pipeline to identify biomarkers for IBD from longitudinal metagenomic data. Furthermore, using a variety of SB approaches, we were able to interpret the predicted key microbial features and communities by inferring connections between microbial and host proteins. This pipeline will enable us to analyse vast amounts of patient microbiome data in the context of clinical and metagenomic data, to allow identification of biomarkers for disease subtypes.