The past several decades of biomedical research have been focused on generating new tools for the collection of biological data including: genetics, genomics, proteomics, clinical laboratory data, electronic health records, metabolomics, epidemiological data, pharmacogenomics, etc. This explosion of ‘omics has led to a wealth of information just waiting to be interrogated. To achieve our goals of identifying the etiology and treatment for diseases of public health interest, it is time to change the way we think about data analysis. We have been hindered in our ability to exploit these laboratory advances because strategies for analyzing these data have not kept pace. An integrative approach is needed that accommodates multiple analytical methods and/or multiple data sources/types to maximize our information extraction. It is my vision that the future of biomedical research will embrace this integrative and collaborative approach for the dissection of common, complex disease.
Meta-dimensional analysis is new field of biomedical research that includes the integration of data from multiple ‘omics of biology. The rationale depends on the fact that any single underlying analytical scheme in one data type will reveal some important results and that multiple analytical approaches and multiple datasets will reveal different subsets of important and potentially novel results. Once results are obtained from any single dataset or analytical approach, these results can be viewed in light of the results from other analyses to best understand the full meaning of the data.
This paradigm shift is EXTREMELY important as with all of the resources that have gone into the dissection of common, complex diseases, little is currently known about the biology of such disorders. We must change the way we interrogate these data in radical ways so that we can begin to unravel the complexities of diseases such as pharmacogenomics, cardiovascular disease, neurodevelopmental disorders, and cancer.
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MS, Applied Statistics, Vanderbilt University, 2002
PhD, Statistical Genetics, Vanderbilt University, 2004