Structural localization of anatomy provides an essential framework to develop image-derived biomarkers, perform image quantification, assess longitudinal changes within a patient, and understand group differences through imaging.
Integration of complex interacting mechanisms is needed to fully understand how toxic environmental contaminants cause human diseases. Proving association of exposure with risk may help formulate polices that identify the exposures or exposure levels to avoid, but they cannot address reducing disease burden in those who were unaware of exposures or when exposures cannot be reduced below safe levels. Mechanistic studies can identify
Sequencing applications such as Whole Genome Seq (WGS), Whole Exome Seq (WES), ChiP Seq, RNA Seq and others are revolutionizing life science research. However, analysis of the Big Data produced from these diverse applications require specialized skills in genomics and create data analysis bottlenecks for most research laboratories.
Autism spectrum disorder is a lifelong neurodevelopmental disorder that is typically diagnosed by 2-3 years of age. Despite the early age of clinical diagnosis, relatively few neuroimaging studies have focused on evaluating the neural basis of autism in very young infants and children. The identification of imaging markers of ASD that precede clinical diagnosis could have great impact in identifying infants at risk for ASD and initiating early interventions.
In this talk, translational informatics methods will be presented for the drug interaction research. They include literature based drug interaction discovery, drug interaction signal data mining from health record databases, and pharmacokinetics model based drug interaction simulations. I will use drug interaction induced myopathy as an example to illustrate how the translational informatics are conducted and its clinical impact.
Comparative effectiveness research can be thought of as evaluating which treatment works best for whom and under what circumstances. While observational studies have become increasingly popular for CER; these studies must be carefully designed and analyzed in a way that controls for self-selection bias, where patients and/or physicians select who receives which treatment.