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Methane and fatty acid metabolism pathways are predictive of Low-FODMAP diet efficacy for patients with irritable bowel syndrome

Date: 2021
Journal: Clinical Nutrition

Systems Biology

Objective: Identification of microbiota-based biomarkers as predictors of low-FODMAP diet response and design of a diet recommendation strategy for IBS patients.
Design: We created a compendium of gut microbiome and disease severity data before and after a low-FODMAP diet treatment from published studies followed by unified data processing, statistical analysis and predictive modeling. We employed data-driven methods that solely rely on the compendium data, as well as hypothesis-driven methods that focus on methane and short chain fatty acid (SCFA) metabolism pathways that were implicated in the disease etiology.
Results: The patient’s response to a low-FODMAP diet was predictable using their pre-diet fecal samples with F1 accuracy…

Using word embeddings to learn a better food ontology

Date: 2020
Journal: Frontiers in Artificial Intelligence

AI Machine Learning Systems Biology

Food ontologies require significant effort to create and maintain as they involve manual and time-consuming tasks, often with limited alignment to the underlying food science knowledge. We propose a semi-supervised framework for the automated ontology population from an existing ontology scaffold by using word embeddings. Having applied this on the domain of food and subsequent evaluation against an expert-curated ontology, FoodOn, we observe that the food word embeddings capture the latent relationships and characteristics of foods. The resulting ontology, which utilizes word embeddings trained from the Wikipedia corpus, has an improvement of 89.7% in precision when compared to the expert-curated ontology FoodOn (0.34 vs. 0.18, respectively, p value = 2.6 × 10–138), and it has a 43.6% shorter path distance (hops) between predicted and actual food instances (2.91 vs. 5.16, respectively, p value = 4.7 × 10–84) when compared to other methods.

The computational diet: A review of computational methods across diet, microbiome, and health

Date: 2020
Journal: Frontiers in Microbiology

AI Machine Learning Systems Biology

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health.

Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning

Date: 2019
Journal: Journal of veterinary internal medicine

AI Clinical Machine Learning Systems Biology

Background: Advanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine.
Hypothesis/Objectives: To derive a model to predict the risk of cats developing chronic kidney disease (CKD) using data from electronic health records (EHRs) collected during routine veterinary practice.
Animals: A total of 106 251 cats that attended Banfield Pet Hospitals between January 1, 1995, and December 31, 2017.
Methods: Longitudinal EHRs from Banfield Pet Hospitals were extracted and randomly split into 2 parts. The first 67% of the data were used to build a prediction model, which included feature selection and identification of the optimal neural network type and architecture. The remaining unseen EHRs were used to evaluate the model performance.

Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients

Date: 2019
Journal: Clinical Immunology

AI Clinical Drug Responsiveness Machine Learning Systems Biology

Rheumatoid arthritis (RA) is therapeutically challenging due to patient heterogeneity and variability. Herein we describe a novel integration of RA synovial genome-scale transcriptomic profiling of different patient cohorts that can be used to provide predictive insights on drug responses.
A normalized compendium consisting of 256 RA synovial samples that cover an intersection of 11,769 genes from 11 datasets was build and compared with similar datasets derived from OA patients and healthy controls. Differentially expression genes (DEGs) that were identified in three independent methods were fed into functional network analysis, with subsequent grouping of the samples based on a non-negative matrix factorization method. RA-relevant pathway activation scores and four machine learning classification techniques supported the generation of a predictive model of patient treatment response. We identified 876 up-regulated DEGs including 24 known genetic risk factors and 8 drug targets.

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