Achieving effective biosurveillance with the Nationwide Health Information Network

The federal government is currently developing the Nationwide Health Information Network (NHIN). Described as a “network of networks,” the NHIN seeks to provide a nationwide, interoperable health information infrastructure that will securely connect consumers with those involved in health care. As part of the national health information technology (HIT) agenda, the NHIN aims to improve individual and population health by enabling health information to follow the consumer, be available for clinical decision-making, and support important public health measures such as biosurveillance. While the NHIN promises to improve clinical care to individuals and to reduce U.S. health care system costs overall, this electronic environment presents novel challenges for protecting individually identifiable health information. A major barrier to achieving public trust in the NHIN is the development of, and adherence to, a consistent and coordinated approach to privacy and security of health information. This paper will analyze the policy framework for electronic health information exchange with the NHIN. This exercise will demonstrate that the current policy is an effective framework for achieving effective biosurveillance with the NHIN. ^

Network-based analysis of genome-wide scans of natural selection

The genomic era brought by recent advances in the next-generation sequencing technology makes the genome-wide scans of natural selection a reality. Currently, almost all the statistical tests and analytical methods for identifying genes under selection was performed on the individual gene basis. Although these methods have the power of identifying gene subject to strong selection, they have limited power in discovering genes targeted by moderate or weak selection forces, which are crucial for understanding the molecular mechanisms of complex phenotypes and diseases. Recent availability and rapid completeness of many gene network and protein-protein interaction databases accompanying the genomic era open the avenues of exploring the possibility of enhancing the power of discovering genes under natural selection. The aim of the thesis is to explore and develop normal mixture model based methods for leveraging gene network information to enhance the power of natural selection target gene discovery. The results show that the developed statistical method, which combines the posterior log odds of the standard normal mixture model and the Guilt-By-Association score of the gene network in a naïve Bayes framework, has the power to discover moderate/weak selection gene which bridges the genes under strong selection and it helps our understanding the biology under complex diseases and related natural selection phenotypes.^