Matti O. Ruuskanen

The paper introduces BioSIFTR, a tool that uses shallow shotgun sequencing and biome-specific databases to generate accurate taxonomic and functional profiles, validated against deep sequencing data.

This paper reviews data science methods for longitudinal multi-omics in microbiome research, highlighting the need for integrated approaches and the lack of widely adopted methods.

This study integrates polygenic risk scores and gut metagenomic data to improve prediction of common diseases, showing enhanced predictive performance over conventional risk factors.

This study uses gut microbiome data and machine learning to improve liver disease risk prediction, showing that combining microbiome features with conventional risk factors enhances model performance.