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- Creators: Almalih, Sara
Description
This work challenges the conventional perceptions surrounding the utility and use of the CMS Open Payments data. I suggest unconsidered methodologies for extracting meaningful information from these data following an exploratory analysis of the 2014 research dataset that, in turn, enhance its value as a public good. This dataset is favored for analysis over the general payments dataset as it is believed that generating transparency in the pharmaceutical and medical device R&D process would be of the greatest benefit to public health. The research dataset has been largely ignored by analysts and this may be one of the few works that have accomplished a comprehensive exploratory analysis of these data. If we are to extract valuable information from this dataset, we must alter both our approach as well as focus our attention towards re-conceptualizing the questions that we ask. Adopting the theoretical framework of complex systems serves as the foundation for our interpretation of the research dataset. This framework, in conjunction with a methodological toolkit for network analysis, may set a precedent for the development of alternative perspectives that allow for novel interpretations of the information that big data attempts to convey. By thus proposing a novel perspective in interpreting the information that this dataset contains, it is possible to gain insight into the emergent dynamics of the collaborative relationships that are established during the pharmaceutical and medical device R&D process.
ContributorsVelez-Cruz, Nayely Luz (Author) / Laubichler, Manfred (Thesis director) / Van Der Leeuw, Sander (Committee member) / School of Sustainability (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Big Data Network Analysis of Genetic Variation and Gene Expression in Individuals with Breast Cancer
Description
The advent of big data analytics tools and frameworks has allowed for a plethora of new approaches to research and analysis, making data sets that were previously too large or complex more accessible and providing methods to collect, store, and investigate non-traditional data. These tools are starting to be applied in more creative ways, and are being used to improve upon traditional computation methods through distributed computing. Statistical analysis of expression quantitative trait loci (eQTL) data has classically been performed using the open source tool PLINK - which runs on high performance computing (HPC) systems. However, progress has been made in running the statistical analysis in the ecosystem of the big data framework Hadoop, resulting in decreased run time, reduced storage footprint, reduced job micromanagement and increased data accessibility. Now that the data can be more readily manipulated, analyzed and accessed, there are opportunities to use the modularity and power of Hadoop to further process the data. This project focuses on adding a component to the data pipeline that will perform graph analysis on the data. This will provide more insight into the relation between various genetic differences in individuals with breast cancer, and the resulting variation - if any - in gene expression. Further, the investigation will look to see if there is anything to be garnered from a perspective shift; applying tools used in classical networking contexts (such as the Internet) to genetically derived networks.
ContributorsRandall, Jacob Christopher (Author) / Buetow, Kenneth (Thesis director) / Meuth, Ryan (Committee member) / Almalih, Sara (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12