Predictive models for integrating clinical and genomic data

Until the last decade, traditional clinical care andmanagement of complex diseasesmainly relied on different clinico-pathological data, such as signs and symptoms, demographic data, pathology results, and medical images. In addition, efforts have been made to capture genetic factors by examining the family history of patients. The effect of such clinical and histo-pathological markers is assessed by cohort-based studies conducted on large populations [115] and the knowledge obtained from these studies is summarized in clinical guidelines for the diagnosis, prognosis, monitoring, and treatment of human disease, e.g., NPI [50] and Adjuvant! Online [56, 119] for breast cancer and palmOne [12] for prostate cancer. However, this approach still falls short. For example, there are adverse drug reactions for some patients who have risk factors similar to those patients who have been cured by the same therapeutic treatment. This issue stems from the strategy of one drug fits all and motivates the need to improve on conclusions drawn from cohort-based studies so that the underlying mechanism of complex diseases can be understood at the individual patient level.