Human Gene Expression and the Protein-Protein Interaction Network: Identification of Potential Disease Module Association to Differential Gene Expression for Patient-to-Drug Matching

The relationship between human gene expression (GE) and precision medicine applications is central in understanding how patients are affected by and how to better treat a disease. Even though it is of extreme importance, this knowledge is still absent from almost any disease analysis resulting in misdiagnosis and mistreatment based on symptomatic and physical observation criteria devoid of high throughput sequencing technologies. By detecting disease modules in the protein-protein interaction (PPI) network, along with the identification of patient differential GE sets (DGE), we can suggest effective individual treatment options. Here, we collect public RNA sequencing data for a diverse population and arrange unique GE patient profiles. DGEs are computed using machine learning techniques and are identified within the PPI network, where disease modules can be pinpointed. Those modules can be used for disease classification specific to expression levels and cohort phenotype. By elucidating these interactions using network approaches with an individual patient’s features, human diseases can be identified not by peripheral approaches, but by a personal genetic diagnosis; essentially redefining disease diagnostics from a “one size fits all” philosophy, to a “one size fits you” reality.