Machine learning and network analysis with focus on the biofilm in .

Research on biofilm formation in 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 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 developed online prediction platform, important functional modules, potential biofilm-associated proteins, and subnetworks of the biofilm-formation pathway were uncovered. Additionally, several novel protein interactions within these functional modules were identified by constructing a protein-protein interaction (PPI) network. To make this data more straightforward for experimental biologists, an online database named SAdb was developed (http://sadb.biownmcli.info/), which integrates gene annotations, transcriptomics, and proteomics data. Thus, the current study will be of interest to researchers in the field of bacteriology, particularly those studying biofilms, which play a crucial role in bacterial growth, pathogenicity, and drug resistance.