Deepdefense: annotation of immune systems in prokaryotes using deep learning.

ABSTRACT:

Background

Due to a constant evolutionary arms race, archaea and bacteria have evolved an abundance and diversity of immune responses to protect themselves against phages. Since the discovery and application of CRISPR-Cas adaptive immune systems, numerous novel candidates for immune systems have been identified. Previous approaches to identifying these new immune systems rely on hidden Markov model (HMM)-based homolog searches or use labor-intensive and costly wet-lab experiments. To aid in finding and classifying immune systems genomes, we use machine learning to classify already known immune system proteins and discover potential candidates in the genome. Neural networks have shown promising results in classifying and predicting protein functionality in recent years. However, the

SUBMITTER: Hauns S 

PROVIDER: S-EPMC11959188 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature