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The aims of clinical and translational research are to achieve a better understanding of the pathogenesis of human disease in order to develop effective diagnostic, therapeutic and prevention strategies. Biomedical informatics can play an important role in supporting this research by facilitating the management, integration, analysis and exchange of data derived from and related to the research problems being studied. A key aspect of this support is to bring clarity, rigor and formalism to the representation of 1. disease initiation, progression, pathogenesis, signs, symptoms, assessments, clinical and laboratory findings, disease diagnosis, treatment, treatment response and outcome, and 2. the interrelations between these distinct entities both in patient management and in clinical research, thus allowing the data to be more readily retrievable and shareable, and more able to serve in the support of algorithmic reasoning.

Every hour, HealthMap, an infectious disease-tracking Web site, culls through news Web sites, public health list servs, the World Health Organization's online pages, and other Web sites in six different languages to pinpoint outbreaks of disease that real-world doctors can then act on.

# The primary intention of creating aTags is not the categorization of the document, but the representation of the key facts inside the document. Key facts in the biomedical domain might be, for example, "Protein A interacts with protein B" or "Overexpression of protein A in tissue B is the cause of disease C". # An aTag is comprised of a set of associated entities. The size of the set is arbitrary, but will typically lie between 2 and 5 entities. For example, the fact "Protein A binds to protein B" can be represented with an aTag comprising of the three entities "Protein A", "Molecular interaction" and "Protein B". Similarly, the fact "Overexpression of protein A in tissue B is the cause of disease C" can be represented with an aTag comprising of the four entities "Overexpression", "Protein A", "Tissue B" and "Disease C". # Each document or database entry can be described with an arbitrary number of such aTags. Each aTag can be associated with the relevant portions of text or data in a fine granularity. # The entities in an aTag are not simple strings, but resources that are part of ontologies and RDF/OWL-enabled databases. For example, "Protein A" and "Protein B" are resources that are defined in the UniProt database, whereas "Molecular Interaction" is a class in the branch of biological processes of the Gene Ontology. They are identified with their URIs.

HealthMap Global disease alert map

Current global disease control focuses almost exclusively on responding to epidemics after they have already spread globally. Nevertheless, dramatic failures in such pandemic control, such as the ongoing lack of success in HIV vaccine development twenty-five years into the pandemic, have shown that this wait-and-respond approach is not sufficient, and that the development of systems to prevent novel pandemics before they are established should be considered a human health imperative. Had we had such systems in place thirty years ago we may have averted the HIV pandemic.

The International HapMap Project is a partnership of scientists and funding agencies from Canada, China, Japan, Nigeria, the United Kingdom and the United States to develop a public resource that will help researchers find genes associated with human disease and response to pharmaceuticals.

"The Institute of the Future will soon be launching what it calls the first massively multiplayer forecast game, billed as The Superstruct Game. According to the game's FAQ, the idea is to 'imagine how we might solve the problems we'll face.' Interestingly, the game itself is meant to be played 'on forums, blogs, videos, wikis, and other familiar online spaces.' From the IFTF website's sneak peak, the game is set in the year 2019, where the Global Extinction Awareness System (GEAS) has forecast the possibility of human extinction by the year 2042 as the result of five simultaneous 'super-threats:' Quarantine, which is a result from 'declining health and pandemic disease;' Ravenous, which relates to the global collapse of the world food system; Power Struggle, related to the flux of power 'as nations fight for energy supremacy and the world searches for alternative energy solutions;' Outlaw Planet, covering increased surveillance and loss of liberties; and, lastly, Generation Exile, which covers the massive increase in refugees."

The prototype we describe is a biomedical knowledge base, constructed for a demonstration at Banff WWW2007 , that integrates 15 distinct data sources using currently available Semantic Web technologies such as the W3C standard Web Ontology Language [OWL] and Resource Description Framework [RDF]. This report outlines which resources were integrated, how the knowledge base was constructed using free and open source triple store technology, how it can be queried using the W3C Recommended RDF query language SPARQL [SPARQL], and what resources and inferences are involved in answering complex queries. While the utility of the knowledge base is illustrated by identifying a set of genes involved in Alzheimer's Disease, the approach described here can be applied to any use case that integrates data from multiple domains.

Bringing Wisdom to Emerging Technology We are at an amazing and critical juncture in human history. If we continue on our current path, we face many potential disasters: overpopulation, shortages of water, oil, and raw materials, financial instability, disease, terrorism, war, and the destruction of species and ecosystems (eg. see Jeffrey Sachs "Common Wealth: Economics for a Crowded Planet"). But the next few decades will also bring advances in science and technology which may dramatically improve our lives. It is remarkable that we are simultaneously on the verge of major revolutions in biology, neuroscience, psychology, artificial intelligence, nanotechnology, and fundamental physics.

CollabRx applies collaborative science to slash the time, cost and risk of therapy development. CollabRx builds and operates Virtual Biotechs for foundations and patients who urgently seek cures for their diseases. Working with these foundations and research institutions, CollabRx * builds teams of top researchers * facilitates planning of a strategic road map * brings best practices to therapy development * manages the execution of the plan The CollabRx research platform connects researchers to one another and to a network of scientific services, providing unprecedented opportunities for knowledge sharing and economies of scale.

Drug Ontology is developed by the nosology project at Stanford Center for Biomedical Informatics Research. This project seeks to discover new therapeutic uses and adverse effects of drugs by finding diseases that have gene expression profiles similar to those of the known indications and adverse effects of drugs. The objectives of the Drug Ontology are: * defines a core set of concepts and relationships that allow us to integrate information from multiple sources, * provides classification services along multiple axes, * provides links to external sources so that data not in the ontology can be queried from these sources.

The RCSB PDB provides a variety of tools and resources for studying the structures of biological macromolecules and their relationships to sequence, function, and disease.

SWAN (Semantic Web Applications in Neuromedicine) is a project to develop knowledge bases for the neurodegenerative disease research communities, using the energy and self-organization of that community enabled by Semantic Web technology. Created in collaboration with the Alzforum and other partners. Read more about the SWAN project here.

Use the idle time on your computer (Windows, Mac, or Linux) to cure diseases, study global warming, discover pulsars, and do many other types of scientific research.