An open source infrastructure for managing knowledge and finding potential collaborators in a domain-specific subset of PubMed, with an example from human genome epidemiology

<p>Abstract</p> <p>Background</p> <p>Identifying relevant research in an ever-growing body of published literature is becoming increasingly difficult. Establishing domain-specific knowledge bases may be a more effective and efficient way to manage and query information within specific biomedical fields. Adopting controlled vocabulary is a critical step toward data integration and interoperability in any information system. We present an open source infrastructure that provides a powerful capacity for managing and mining data within a domain-specific knowledge base. As a practical application of our infrastructure, we presented two applications – Literature Finder and Investigator Browser – as well as a tool set for automating the data curating process for the human genome published literature database. The design of this infrastructure makes the system potentially extensible to other data sources.</p> <p>Results</p> <p>Information retrieval and usability tests demonstrated that the system had high rates of recall and precision, 90% and 93% respectively. The system was easy to learn, easy to use, reasonably speedy and effective.</p> <p>Conclusion</p> <p>The open source system infrastructure presented in this paper provides a novel approach to managing and querying information and knowledge from domain-specific PubMed data. Using the controlled vocabulary UMLS enhanced data integration and interoperability and the extensibility of the system. In addition, by using MVC-based design and Java as a platform-independent programming language, this system provides a potential infrastructure for any domain-specific knowledge base in the biomedical field.</p

BMC Bioinformatics

BackgroundIdentifying relevant research in an ever-growing body of published literature is becoming increasingly difficult. Establishing domain-specific knowledge bases may be a more effective and efficient way to manage and query information within specific biomedical fields. Adopting controlled vocabulary is a critical step toward data integration and interoperability in any information system. We present an open source infrastructure that provides a powerful capacity for managing and mining data within a domain-specific knowledge base. As a practical application of our infrastructure, we presented two applications \ue2\u20ac\u201c Literature Finder and Investigator Browser \ue2\u20ac\u201c as well as a tool set for automating the data curating process for the human genome published literature database. The design of this infrastructure makes the system potentially extensible to other data sources.ResultsInformation retrieval and usability tests demonstrated that the system had high rates of recall and precision, 90% and 93% respectively. The system was easy to learn, easy to use, reasonably speedy and effective.ConclusionThe open source system infrastructure presented in this paper provides a novel approach to managing and querying information and knowledge from domain-specific PubMed data. Using the controlled vocabulary UMLS enhanced data integration and interoperability and the extensibility of the system. In addition, by using MVC-based design and Java as a platform-independent programming language, this system provides a potential infrastructure for any domain-specific knowledge base in the biomedical field

BMC Bioinformatics

BackgroundMillions of single nucleotide polymorphisms have been identified as a result of the human genome project and the rapid advance of high throughput genotyping technology. Genetic association studies, such as recent genome-wide association studies (GWAS), have provided a springboard for exploring the contribution of inherited genetic variation and gene/environment interactions in relation to disease. Given the capacity of such studies to produce a plethora of information that may then be described in a number of publications, selecting possible disease susceptibility genes and identifying related modifiable risk factors is a major challenge. A Web-based application for finding evidence of such relationships is key to the development of follow-up studies and evidence for translational research.ResultsWe compared Gene Prospector results for the query "Parkinson" with a list of 13 leading candidate genes (Top Results) from a curated, specialty database for genetic associations with Parkinson disease (PDGene). Nine of the thirteen leading candidate genes from PDGene were in the top 10th percentile of the ranked list from Gene Prospector. In fact, Gene Prospector included more published genetic association studies for the 13 leading candidate genes than PDGene did.ConclusionGene Prospector provides an online gateway for searching for evidence about human genes in relation to diseases, other phenotypes, and risk factors, and provides links to published literature and other online data sources. Gene Prospector can be accessed via

Trends in Population-Based Studies of Human Genetics in Infectious Diseases

Pathogen genetics is already a mainstay of public health investigation and control efforts; now advances in technology make it possible to investigate the role of human genetic variation in the epidemiology of infectious diseases. To describe trends in this field, we analyzed articles that were published from 2001 through 2010 and indexed by the HuGE Navigator, a curated online database of PubMed abstracts in human genome epidemiology. We extracted the principal findings from all meta-analyses and genome-wide association studies (GWAS) with an infectious disease-related outcome. Finally, we compared the representation of diseases in HuGE Navigator with their contributions to morbidity worldwide. We identified 3,730 articles on infectious diseases, including 27 meta-analyses and 23 GWAS. The number published each year increased from 148 in 2001 to 543 in 2010 but remained a small fraction (about 7%) of all studies in human genome epidemiology. Most articles were by authors from developed countries, but the percentage by authors from resource-limited countries increased from 9% to 25% during the period studied. The most commonly studied diseases were HIV/AIDS, tuberculosis, hepatitis B infection, hepatitis C infection, sepsis, and malaria. As genomic research methods become more affordable and accessible, population-based research on infectious diseases will be able to examine the role of variation in human as well as pathogen genomes. This approach offers new opportunities for understanding infectious disease susceptibility, severity, treatment, control, and prevention

2007 program review

Public health genomics is a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health. Public health genomics uses population-based data on genetic variation and gene- environment interactions to develop evidence-based tools for improving health and preventing disease.Through the National Office of Public Health Genomics (NOPHG), the Centers for Disease Control and Prevention (CDC) provides national and international leadership in public heath genomics, while building partnerships with other federal agencies, state health departments, public health organizations, professional groups, and the private sector.National Office of Public Health Genomics -- Organization and Staffing -- Strategic Accomplishments in FY2007 -- Scientific Highlights in FY2007 -- NOPHG-Funded State Achievements in FY2007 -- Future Directions -- Publications List.2007736

Type 2 diabetes genetic association database manually curated for the study design and odds ratio

<p>Abstract</p> <p>Background</p> <p>The prevalence of type 2 diabetes has reached epidemic proportions worldwide, and the incidence of life-threatening complications of diabetes through continued exposure of tissues to high glucose levels is increasing. Advances in genotyping technology have increased the scale and accuracy of the genotype data so that an association genetic study has expanded enormously. Consequently, it is difficult to search the published association data efficiently, and several databases on the association results have been constructed, but these databases have their limitations to researchers: some providing only genome-wide association data, some not focused on the association but more on the integrative data, and some are not user-friendly. In this study, a user-friend database of type 2 diabetes genetic association of manually curated information was constructed.</p> <p>Description</p> <p>The list of publications used in this study was collected from the HuGE Navigator, which is an online database of published genome epidemiology literature. Because type 2 diabetes genetic association database (T2DGADB) aims to provide specialized information on the genetic risk factors involved in the development of type 2 diabetes, 701 of the 1,771 publications in the type 2 Diabetes case-control study for the development of the disease were extracted.</p> <p>Conclusions</p> <p>In the database, the association results were grouped as either positive or negative. The gene and SNP names were replaced with gene symbols and rsSNP numbers, the association p-values were determined manually, and the results are displayed by graphs and tables. In addition, the study design in publications, such as the population type and size are described. This database can be used for research purposes, such as an association and functional study of type 2 diabetes related genes, and as a primary genetic resource to construct a diabetes risk test in the preparation of personalized medicine in the future.</p

Cloud-based genomics pipelines for ophthalmology: Reviewed from research to clinical practice

Aim: To familiarize clinicians with clinical genomics, and to describe the potential of cloud computing for enabling the future routine use of genomics in eye hospital settings. Design: Review article exploring the potential for cloud-based genomic pipelines in eye hospitals. Methods: Narrative review of the literature relevant to clinical genomics and cloud computing, using PubMed and Google Scholar. A broad overview of these fields is provided, followed by key examples of their integration. Results: Cloud computing could benefit clinical genomics due to scalability of resources, potentially lower costs, and ease of data sharing between multiple institutions. Challenges include complex pricing of services, costs from mistakes or experimentation, data security, and privacy concerns. Conclusions and future perspectives: Clinical genomics is likely to become more routinely used in clinical practice. Currently this is delivered in highly specialist centers. In the future, cloud computing could enable delivery of clinical genomics services in non-specialist hospital settings, in a fast, cost-effective way, whilst enhancing collaboration between clinical and research teams

Research Data Management Practices And Impacts on Long-term Data Sustainability: An Institutional Exploration

With the \u27data deluge\u27 leading to an institutionalized research environment for data management, U.S. academic faculty have increasingly faced pressure to deposit research data into open online data repositories, which, in turn, is engendering a new set of practices to adapt formal mandates to local circumstances. When these practices involve reorganizing workflows to align the goals of local and institutional stakeholders, we might call them \u27data articulations.\u27 This dissertation uses interviews to establish a grounded understanding of the data articulations behind deposit in 3 studies: (1) a phenomenological study of genomics faculty data management practices; (2) a grounded theory study developing a theory of data deposit as articulation work in genomics; and (3) a comparative case study of genomics and social science researchers to identify factors associated with the institutionalization of research data management (RDM). The findings of this research offer an in-depth understanding of the data management and deposit practices of academic research faculty, and surfaced institutional factors associated with data deposit. Additionally, the studies led to a theoretical framework of data deposit to open research data repositories. The empirical insights into the impacts of institutionalization of RDM and data deposit on long-term data sustainability update our knowledge of the impacts of increasing guidelines for RDM. The work also contributes to the body of data management literature through the development of the data articulation framework which can be applied and further validated by future work. In terms of practice, the studies offer recommendations for data policymakers, data repositories, and researchers on defining strategies and initiatives to leverage data reuse and employ computational approaches to support data management and deposit

MRC Population Data Archiving and Access Project

There are several important motivations for preserving research data: scientific, historical, economic, and legal. There is also significant demand for the re-use of population-based data in the medical research area, as usage of medical/health studies at the UK Data Archive (UKDA) show. This project investigated opportunities and barriers for the MRC in developing data archiving and access policy. Through case studies and interviews it sought to gain a better understanding of the range and variation of current MRC-funded data creation activities, the existing data management infrastructure and practice in MRC-funded contexts, and the views and opinions of those likely to be most affected by the establishment of such a policy

Precision health approaches: ethical considerations for health data processing

This thesis provides insights and recommendations on some of the most crucial elements necessary for an effective, legally and ethically sound implementation of precision health approaches in the Swiss context (and beyond), specifically for precision medicine and precision public health. In this regard, this thesis recognizes the centrality of data in these two abovementioned domains, and the ethical and scientific imperative of ensuring the widespread and responsible sharing of high quality health data between the numerous stakeholders involved in healthcare, public health and associated research domains. It also recognizes the need to protect not only the interests of data subjects but also those of data processors. Indeed, it is only through a comprehensive assessment of the needs and expectations of each and every one regarding data sharing activities that sustainable solutions to known ethical and scientific conundrums can be devised and implemented. In addition, the included chapters in this thesis emphasize recommending solutions that could be convincingly applied to real world problems, with the ultimate objective of having a concrete impact on clinical and public health practice and policies, including research activities. Indeed, the strengths of this thesis reside in a careful and in-depth interdisciplinary assessment of the different issues at stake in precision health approaches, with the elaboration of the least disruptive solutions (as far as possible) and recommendations for an easy evaluation and subsequent adoption by relevant stakeholders active in these two domains. This thesis has three main objectives, namely (i) to investigate and identify factors influencing the processing of health data in the Swiss context and suggest some potential solutions and recommendations. A better understanding of these factors is paramount for an effective implementation of precision health approaches given their strong dependence on high quality and easily accessible health datasets; (ii) to identify and explore the ethical, legal and social issues (ELSI) of innovative participatory disease surveillance systems – also falling under precision health approaches – and how research ethics are coping within this field. In addition, this thesis aims to strengthen the ethical approaches currently used to cater for these ELSIs by providing a robust ethical framework; and lastly, (iii) to investigate how precision health approaches might not be able to achieve their social justice and health equity goals, if the impact of structural racism on these initiatives is not given due consideration. After a careful assessment, this thesis provides recommendations and potential actions that could help these precision health approaches adhere to their social justice and health equity goals. This thesis has investigated these three main objectives using both empirical and theoretical research methods. The empirical branch consists of systematic and scoping reviews, both adhering to the PRISMA guidelines, and two interview-based studies carried out with Swiss expert stakeholders. The theoretical branch consists of three chapters, each addressing important aspects concerning precision health approaches