Abstract: Life sciences research that uses genetic resources is increasingly collaborative and global, yet collective action remains a significant barrier to the creation and management of shared research resources. These resources include sequence data and associated metadata, and biological samples, and can be understood as a type of knowledge commons. Collective action by stakeholders to create and use knowledge commons for research has potential benefits for all involved, including minimizing costs and sharing risks, but there are gaps in our understanding of how institutional arrangements may promote such collective action in the context of global genetic resources. We address this research gap by examining the attributes of an exemplar global knowledge commons: The DNA barcode commons. DNA barcodes are short, standardized gene regions that can be used to inexpensively identify unknown specimens, and proponents have led international efforts to make DNA barcodes a standard species identification tool. Our research examined if and how attributes of the DNA barcode commons, including governance of DNA barcode resources and management of infrastructure, facilitate global participation in DNA barcoding efforts. Our data sources included key informant interviews, organizational documents, scientific outputs of the DNA barcoding community, and DNA barcode record submissions. Our research suggested that the goal of creating a globally inclusive DNA barcode commons is partially impeded by the assumption that scientific norms and expectations held by researchers in high income countries are universal. We found scientific norms are informed by a complex history of resource misappropriation and mistrust between stakeholders. DNA barcode organizations can mitigate the challenges caused by its global membership through creating more inclusive governance structures, developing norms for the community are specific to the context of DNA barcoding, and through increasing awareness and knowledge of pertinent legal frameworks.
“There is research of all types on Get The Research. In this early release it specializes in research in biology and medicine (papers indexed by PubMed) — this will be widened further in the future….
How do I know what research I can trust?
This is a great question. Get The Research flags each article with its “level of evidence” when we know it — is the article just a report about a single patient (a “case study”) or a more trustworthy analysis combining the results of many studies (a “meta-analysis”)? Click on the tags above the article titles to learn more. We rank articles with higher levels of evidence higher in the search results to make these easier to find. Reading the news studies about a paper (linked to from the “Learn More” page when we’ve found news articles) is a great way to find out what others think about the results….”
“Just as software code can be open source rather than proprietary, so there are publicly funded genomic sequencing initiatives that make their results available to all. One of the largest projects, the UK Biobank(UKB), involves 500,000 participants. Any researcher, anywhere in the world, can download complete, anonymized data sets, provided they are approved by the UKB board. One important restriction is that they must not try to re-identify any participant—something that would be relatively easy to do given the extremely detailed clinical history that was gathered from volunteers along with blood and urine samples. Investigators asked all 500,000 participants about their habits, and examined them for more than 2,000 different traits, including data on their social lives, cognitive state, lifestyle and physical health.
Given the large number of genomes that need to be sequenced, the first open DNA data sets from UKB are only partial, although the plan is to sequence all genomes more fully in due course. These smaller data sets allow what is called “genotyping”, which provides a rough map of a person’s DNA and its specific properties. Even this partial sequencing provides valuable information, especially when it is available for large numbers of people. As an article in Science points out, it is not just the size and richness of the open data sets that makes the UK Biobank unique, it is the thorough-going nature of the sharing that is required from researchers….
It’s the classic “given enough eyeballs, all bugs are shallow”. By open-sourcing the genomic code of 500,000 of its citizens, the UK is getting the top DNA hackers in the world to find the “bugs”—the variants that are associated with medical conditions—that will help our understanding of them and may well lead to the development of new treatments for them. The advantages are so obvious, it’s a wonder people use anything else. A bit like open source….”
“But what if there was another way of developing the medicines we need? A way that eschews market incentives that stop pharma companies from developing medicines for diseases of poverty and does away with the secrecy that shrouds drug development.
According to advocates of open source pharma, there is….
Inspired by the open source movement in software, open drug discovery projects make their data and ideas available on the internet to anyone.
Matthew Todd, a professor of drug discovery at University College, London and one of the founders of the open source pharmamovement believes that open source could potentially transform the way we find cures….”
“Failure to report the results of clinical trials threatens the public’s trust in research and the integrity of the medical literature, and should be considered academic misconduct at the individual and institutional levels. According to the ethical principles for research outlined in the Declaration of Helsinki, researchers “have a duty to make publicly available the results of their research on human subjects and are accountable for the completeness and accuracy of their reports” (1). When participants volunteer to take part in clinical trials, and expose themselves to interventions with unknown safety and efficacy profiles, they have a tacit assumption, based on trust, that the evidence generated will inform clinical science (2). Health care providers and medical societies, who are responsible for evaluating and synthesizing evidence and filling the gap between research and practice, need for investigators to fully report their results in a timely manner. The utility of the diligent search for truth in the medical literature depends on its completeness. However, when research findings are not consistently disseminated, the literature provides a skewed view of the science, which may bias reviews of the evidence….
The conduct of research in humans comes with inviolable responsibilities, including the commitment to share what has been learned. No reason exists for the topline results of a clinical trial not to be made public. Failure to report is detrimental to the scientific process. When trial results are not publicly available for years after study completion, patients, institutional review boards, clinicians, researchers, and the public must rely on incomplete evidence, which may lead to misconceptions about the efficacy and safety of interventions. The time has arrived to address this threat to trust and science.”
While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. We argue curated knowledge bases will remain an important component of future diagnostic decision support systems by providing ground truth and facilitating explainable human-computer interaction, but that prototype development is hampered by the lack of freely available computable knowledge bases.
We constructed an open access knowledge base and evaluated its potential in the context of a prototype decision support system. We developed a modified set-covering algorithm to benchmark the performance of our knowledge base compared to existing platforms. Testing was based on case reports from selected literature and medical student preparatory material.
The knowledge base contains over 2000 ICD-10 coded diseases and 450 RX-Norm coded medications, with over 8000 unique observations encoded as SNOMED or LOINC semantic terms. Using 117 medical cases, we found the accuracy of the knowledge base and test algorithm to be comparable to established diagnostic tools such as Isabel and DXplain. Our prototype, as well as DXplain, showed the correct answer as “best suggestion” in 33% of the cases. While we identified shortcomings during development and evaluation, we found the knowledge base to be a promising platform for decision support systems.
We built and successfully evaluated an open access knowledge base to facilitate the development of new medical diagnostic assistants. This knowledge base can be expanded and curated by users and serve as a starting point to facilitate new technology development and system improvement in many contexts.
- • Machine learning algorithms have shown promising results but the number of clinically successful AI products is limited.
- • Access to appropriate data for training, testing and evaluation is a key limitation to the field.
- • Open access repositories are a vital source of quality data needed for training and testing machine learning algorithms….”
“How concerned should we, researchers, authors, editors, and readers, be about Plan S? The answer is not clear. Journals such as Nursing Research have an important place in the dissemination of scientific findings, advanced methods, and innovative thought. We are committed to providing avenues to immediate (gold) open access for authors who chose that route. We are also committed to green open access for those unable or unwilling to pay gold open access fees. We will remain fully compliant with the requirements of NIH public access policies. We are also, as are most researchers, supportive of public access to our work, and we are concerned that initiatives such as Plan S may restrict where and how we make our work available. We are also concerned about the burden of costs for publication that initiatives such as Plan S support. Who will, in fact, bear the cost of full open access? Authors? Their universities? The NIH or other funders? We just do not know. Perhaps, it will amount to nothing. More likely, however, there will be changes in how we, and all journals, do our work….”
Abstract: Applying toxicogenomics to improving the safety profile of drug candidates and crop protection molecules is most useful when it identifies relevant biological and mechanistic information that highlights risks and informs risk mitigation strategies. Pathway-based approaches, such as GSEA, integrate toxicogenomic data with known biological process and pathways. Network methods help define unknown biological processes and offer data reduction advantages. Integrating the two approaches would improve interpretation of toxicogenomic information. Barriers to the routine application of these methods in genome-wide transcriptomic studies include a need for “hands-on” computer programming experience, the selection of one or more analysis methods (e.g. pathway analysis methods), the sensitivity of results to algorithm parameters, and challenges in linking differential gene expression to variation in safety outcomes. To facilitate adoption and reproducibility of gene expression analysis in safety studies, we have developed Collaborative Toxicogenomics (CTox), an open-access integrated web portal using the Django web framework. The software, developed with the Python programming language, is modular, extensible and implements “best-practice” methods in computational biology. New study results are compared to over 4,000 rodent liver experiments from Drug Matrix and open TG-GATEs. A unique feature of the software is the ability to integrate clinical chemistry and histopathology-derived outcomes with results from gene expression studies, leading to relevant mechanistic conclusions. We describe its application by analyzing the effects of several toxicants on liver gene expression and exemplify application to predicting toxicity study outcomes upon chronic treatment from expression changes in acute-duration studies.