Interpretable metric learning in comparative metagenomics: The adaptive Haar-like distance

Random forests have emerged as a promising tool in comparative metagenomics because they can predict environmental characteristics based on microbial composition in datasets where β -diversity metrics fall short of revealing meaningful relationships between samples. Nevertheless, despite this effica...

Ausführliche Beschreibung