George B. Stefano, Richard M. Kream
Neuroscience Research Institute, State University of New York at Old Westbury, Old Westbury, NY, USA
Med Sci Monit 2015; 21:201-204
Personalized medicine’s foundation rests on the use of molecular technologies, which are being used to identify genetic mutations, polymorphisms, and variants that can be associated with an individual’s genetic make up, revealing risk factors and predictive data. Needless to say this same analysis can be performed on various types of cancers, including samples stored for many years under the right conditions. For the most part, these technologies employ microarray and RNA-Seq methodologies, which examine large numbers of gene expressions at a time, providing clustering and patterns of this expression. The methodologies and their evaluative outcomes further demonstrate that more than a single gene is involved with various phenomena. However, given the mass of data emerging from this analysis, and commonalities they reveal between various phenomena/disorders, achieving 100% certainty may not be that easy. Another outcome from this massive store of molecular data is the concept of one medicine. This field has been developed by researchers in a variety of disciplines (e.g., medical and veterinary science) that advocate for greater integration of animal and human health. One medicine takes advantage of the fact that molecular commonalities in major biochemical pathways occur because of evolutionary conservation, which is dependent on stereospecificity. In this regard, the foci of personalized medicine and one medicine are quite broad and require trained professionals, as well as a lowering of cost in order to be better integrated into mainstream medical practice.
Keywords: Computational Biology - methods, Gene Expression Profiling, Gene Expression Regulation, Genome, Human, Individualized Medicine - methods, Models, Economic, Mutation, Oligonucleotide Array Sequence Analysis - methods, Polymorphism, Genetic, Risk Factors, Sequence Analysis, DNA, Sequence Analysis, RNA, Signal Transduction