Machine learning for data integration in human gut microbiome

Abstract

Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led to an explosion of different types of molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. In this review, we first discuss how dysbiosis of the intestinal microbiota is linked to human disease development and how potential modulation strategies of the gut microbial ecosystem can be used for disease treatment. In addition, we introduce categories and workflows of different machine learning approaches, and how they can be used to perform integrative analysis of multi-omics data. Finally, we review advances of machine learning in gut microbiome applications and discuss related challenges. Based on this we conclude that machine learning is very well suited for analysis of gut microbiome and that these approaches can be useful for development of gut microbe-targeted therapies, which ultimately can help in achieving personalized and precision medicine.

Understanding gut microbiome‐based machine learning platforms: A review on therapeutic approaches using deep learning

“…Development of high‐throughput technologies to generate multi‐omics data, from different human tissues, due to the complex interactions, shows rough insights into the associations between the gut microbiota and the host. (Li et al., 2022). …”

“…The bioinformatic analysis provides a wider approach towards gene predictions and functional annotation which simultaneously form taxonomic composition F I G U R E 4 Development of high-throughput technologies to generate multi-omics data, from different human tissues, due to the complex interactions, shows rough insights into the associations between the gut microbiota and the host. (Li et al, 2022). and functional proportions of the gut microbiome.…”

The Potential of Meta-Proteomics and Artificial Intelligence to Establish the Next Generation of Probiotics for Personalized Healthcare

“…However, applying ML in the stratification of microbiome data is still a big challenge because of the highly heterogeneous data and the form of bacterial metabolites or proteins. Additionally, microbial variation within and between individuals complicates the development of robust and precise prediction models and often leads to an overfitting problem . Specific ML platforms (e.g., iProbiotics, OmicLearn) have overcome these overfitting issues by utilizing the incremental feature selection and RepeatedStratifiedKFold methods, which have attained the maximum accuracy. , Core k-mer features in iProbiotics have provided molecular markers to predict the suitable probiotic strain based on bile tolerance and drug resistance genes .…”

Prediction and causal inference of hyperuricemia using gut microbiota

“…GM data is complex, with potentially influential factors, such as geographic location, ethnicity, stress, age, and lifestyle 9 . Statistics and machine learning can explore and integrate disease-related features from complex data by identifying hidden patterns in correlations, and generating models that can accurately predict phenotypes 10 . Therefore, it has frequently been applied in GM research in recent years.…”