Dataset Information

Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting.

ABSTRACT: The gut microbiome has shown promise as a predictive biomarker for various diseases. However, the potential of gut microbiota for prospective risk prediction of liver disease has not been assessed. Here, we utilized shallow shotgun metagenomic sequencing of a large population-based cohort (N > 7,000) with ∼15 years of follow-up in combination with machine learning to investigate the predictive capacity of gut microbial predictors individually and in conjunction with conventional risk factors for incident liver disease. Separately, conventional and microbial factors showed comparable predictive capacity. However, microbiome augmentation of conventional risk factors using machine learning significantly improved the performance. Similarly, disease-free survival analysis showed significantly improved stratification using microbiome-augmented models. Investigation of predictive microbial signatures revealed previously unknown taxa for liver disease, as well as those previously associated with hepatic function and disease. This study supports the potential clinical validity of gut metagenomic sequencing to complement conventional risk factors for prediction of liver diseases.

SUBMITTER: Liu Y

PROVIDER: S-EPMC9097589 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting.

Liu Yang Y Méric Guillaume G Havulinna Aki S AS Teo Shu Mei SM Åberg Fredrik F Ruuskanen Matti M Sanders Jon J Zhu Qiyun Q Tripathi Anupriya A Verspoor Karin K Cheng Susan S Jain Mohit M Jousilahti Pekka P Vázquez-Baeza Yoshiki Y Loomba Rohit R Lahti Leo L Niiranen Teemu T Salomaa Veikko V Knight Rob R Inouye Michael M

Cell metabolism 20220329 5

The gut microbiome has shown promise as a predictive biomarker for various diseases. However, the potential of gut microbiota for prospective risk prediction of liver disease has not been assessed. Here, we utilized shallow shotgun metagenomic sequencing of a large population-based cohort (N > 7,000) with ∼15 years of follow-up in combination with machine learning to investigate the predictive capacity of gut microbial predictors individually and in conjunction with conventional risk factors for ...[more]

PMID: 35354069

Similar Datasets

Project description:Understanding accurate methods for predicting yields in complex agricultural systems is critical for effective nutrient management and crop growth. Machine learning has proven to be an important tool in this context. Numerous studies have investigated its potential for predicting yields under different conditions. Among these algorithms, Random Forest (RF) has gained prominence due to its ability to manage large data sets with high dimensions, as well as its ability to uncover complicated non-linear relationships and interactions between variables. RF is particularly suitable for scenarios with categorical variables and missing data. Given the complex web of management practices and their nonlinear effects on yield prediction, it is important to investigate new machine learning algorithms. In this context, our study focused on the evaluation of gradient boosting methods, particularly Extreme Gradient Boosting (XGB) and Gradient Boosting Regressor (GBR), as potential candidates for yield estimation of the maize hybrid Zhengdan 958. Our aim was not only to evaluate and compare these algorithms with existing approaches, but also to comprehensively analyze the resulting model uncertainties. Our approach includes comparing multiple machine learning algorithms, developing and selecting suitable features, fine-tuning the models by training and adjusting the hyperparameters, and visualizing the results. Using a recent dataset of over 1700 maize yield data pairs, our evaluation included a spectrum of algorithms. Our results show robust prediction accuracy for all algorithms. In particular, the predictions of XGB (RMSE = 0.37, R2 = 0.87 and MAE = 0.26) and GBR(RMSE = 0.39, R2 = 0.86 and MAE = 0.27), emphasized the central role of weather characteristics and confirmed the high dependence of crop yield prediction on environmental attributes. Utilizing the capabilities of gradient boosting for yield prediction holds immense potential and is consistent with the promise of this method to serve as a catalyst for further investigation in this evolving field.

Dataset Information

Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting.

Publications

Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets