Integration of Clinical and Genomic data: a Methodological Survey
Date of Submission:
February 20, 2013
Human diseases are inherently complex and governed by the complicated interplay of several underlying factors. Clinical research focuses on behavioral, demographic and pathology information, whereas molecular genomics focuses on finding underlying genetic and genomic factors in genomic data collected on mRNA expression, proteomics, biological networks, and other microbiological features. However, each of these clinical and genomic datasets contains information only about one particular aspect of a complex disease, rather than covering all of the several complicated underlying risk factors. This has led to a new area of research that integrates both clinical and genomic data and aims to extract more information about diseases by considering not only all the various factors, but also the interactions among those factors, which cannot be captured by clinical and genomic studies that are performed independently of each other. Although initial efforts have already been made to develop such integrative modeling of the clinical and genomic data to shed light on the biological mechanism of the diseases, the research field is still in a rudimentary stage. In this review article, we survey the general issues, challenges and current work of clinicogenomic studies. We also summarize the current state of the field and discuss some possibilities for future work.