SOURCE-LINKED INTELLIGENCE

Integrative Machine Learning Approaches for Bacterial sRNA Genome Annotation

CORDIS · observation · Publication date unknown

Integrative Machine Learning Approaches for Bacterial sRNA Genome Annotation This project addresses the critical challenge of accurate, genome-wide annotation of bacterial small regulatory RNAs (sRNAs), which are key regulators of microbial adaptation and gene expression. Despite advances in sequencing technologies, many sRNAs remain undetected due to their diverse sequence conservation, complex secondary structures, and variable genomic contexts. The researcher will compile and curate a large and diverse dataset of bacterial genomes with experimentally validated and computationally annotated sRNAs. Novel machine learning models will be developed to improve sRNA prediction accuracy, and these models will be integrated into an open-source annotation pipeline. Rigorous benchmarking will validate model performance, and all algorithms, datasets, and results will be released openly to enhance reproducibi

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recordType

award

status

SIGNED

region

EU

value

189474.24

unit

EUR