AIMC Topic: Prokaryotic Cells

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MetaPred2CS: a sequence-based meta-predictor for protein-protein interactions of prokaryotic two-component system proteins.

Bioinformatics (Oxford, England)
MOTIVATION: Two-component systems (TCS) are the main signalling pathways of prokaryotes, and control a wide range of biological phenomena. Their functioning depends on interactions between TCS proteins, the specificity of which is poorly understood.

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

GigaScience
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, nu...

IPEV: identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning.

GigaScience
BACKGROUND: The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and...

Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.

FEMS microbiology reviews
Annotating protein sequences according to their biological functions is one of the key steps in understanding microbial diversity, metabolic potentials, and evolutionary histories. However, even in the best-studied prokaryotic genomes, not all protei...

NetGO: improving large-scale protein function prediction with massive network information.

Nucleic acids research
Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Bas...