Target/Biomarker selection using systems networks and decision theory

Methods for automated target and biomarker identification typically involve ranking genes and proteins according to indication relevance criteria. A typical process then involves traversing scientific literature for supporting evidence. However, many age-related western world diseases bear the hallmarks of complex heterogeneous conditions that vary along a spectrum of disease severity. They frequently involve multiple systems and pathways. Hence selection pools comprise many hundreds to thousands of genes. Selecting the best targets and stratification biomarkers from a large candidate pool presents a significant challenge in drug discovery. Current approaches are at best semi-automated or rely on expert opinion. This means it is difficult to avoid bias in such decision-making tasks. Decision theory is one branch of Artificial Intelligence that can be applied to help resolve these complexities. Automated reasoning and argumentation theory are two appropriate branches of natural language processing well suited to the task. The aim of this project is to fully automate target and biomarker selections using a combination of NLP-based and recommendation methods. The end goal is to generate unbiased decisions from a complex network of information relationships.