Integration of mRNA and protein expression data for the identification of potential biomarkers associated with pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most death-dealing tumors, with a tremendously poor prognosis. Here, we, through interrogation of mRNA and protein data combined with a system biology approach, identified several key genes, functional processes, and pathways that can have critical roles in PDAC. We detected an interesting module related to the clinical traits that enriched in the ribosome, hematopoietic cell lineage, and cell adhesion molecules-related pathways.

Integration of meta-analysis, machine learning and systems biology approach for investigating the transcriptomic response to drought stress in Populus species.

In Populus, drought is a major problem affecting plant growth and development which can be closely reflected by corresponding transcriptomic changes. Nevertheless, how these changes in Populus are not fully understood. Here, we first used meta-analysis and machine learning methods to identify water stress-responsive genes and then performed a systematic approach to discover important gene networks.