|Title||Chapter 2: Data-driven view of disease biology.|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Greene, CS, Troyanskaya, OG|
|Journal||PLoS Comput Biol|
|Keywords||Bayes Theorem, Cell Line, Disease, Genome, Human, Humans|
Modern experimental strategies often generate genome-scale measurements of human tissues or cell lines in various physiological states. Investigators often use these datasets individually to help elucidate molecular mechanisms of human diseases. Here we discuss approaches that effectively weight and integrate hundreds of heterogeneous datasets to gene-gene networks that focus on a specific process or disease. Diverse and systematic genome-scale measurements provide such approaches both a great deal of power and a number of challenges. We discuss some such challenges as well as methods to address them. We also raise important considerations for the assessment and evaluation of such approaches. When carefully applied, these integrative data-driven methods can make novel high-quality predictions that can transform our understanding of the molecular-basis of human disease.
|Alternate Journal||PLoS Comput. Biol.|