Machine learning and network analysis with focus on the biofilm in <i>Staphylococcus aureus</i>.

ABSTRACT: Research on biofilm formation in Staphylococcus aureus has greatly benefited from the generation of high-throughput sequencing data to drive molecular analysis. The accumulation of high-throughput sequencing data, particularly transcriptomic data, offers a unique opportunity to unearth the network and constituent genes involved in biofilm formation using machine learning strategies and co-expression analysis. Herein, the available RNA sequencing data related to Staphylococcus aureus biofilm studies and identified influenced functional pathways and corresponding genes in the process of the transition of bacteria from planktonic to biofilm state by employing machine learning and differential expression analysis. Using weighted gene co-expression analysis and previously develope

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC11617897 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature