An integrated AI knowledge graph framework of bacterial enzymology and metabolism.

ABSTRACT: The study of bacterial metabolism holds immense significance for improving human health and advancing agricultural practices. The prospective applications of genomically encoded bacterial metabolism present a compelling opportunity, particularly in the light of the rapid expansion of genomic sequencing data. Current metabolic inference tools face challenges in scaling with large datasets, leading to increased computational demands, and often exhibit limited inter-relatability and interoperability. Here, we introduce the Integrated Biosynthetic Inference Suite (IBIS), which employs deep learning models and a knowledge graph to facilitate rapid, scalable bacterial metabolic inference. This system leverages a series of Transformer based models to generate high quality, meaningful embeddings f

SUBMITTER: Spencer NR 

PROVIDER: S-EPMC12012490 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature