Refining biome labeling for large-scale microbial community samples: Leveraging neural networks and transfer learning.

ABSTRACT: Microbiome research has generated an extensive amount of data, resulting in a wealth of publicly accessible samples. Accurate annotation of these samples is crucial for effectively utilizing microbiome data across scientific disciplines. However, a notable challenge arises from the lack of essential annotations, particularly regarding collection location and sample biome information, which significantly hinders environmental microbiome research. In this study, we introduce Meta-Sorter, a novel approach utilizing neural networks and transfer learning, to enhance biome labeling for thousands of microbiome samples in the MGnify database that have incomplete information. Our findings demonstrate that Meta-Sorter achieved a remarkable accuracy rate of 96.7% in classifying samples among the 16,5

SUBMITTER: Wang N 

PROVIDER: S-EPMC10457426 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature