Abstract: Imagine a virtual marketplace or ecosystem where participants share data, knowledge, materials, and services to accelerate research. This Health Commons is a bold vision whose aim is no less than to change the processes, economics, and culture of translating basic research into drugs.

25+ million patients and their families suffer from over 6,000 orphan diseases that are currently uneconomical to address. The time is ripe for a radically different approach that leverages collaboration and the Internet to enable small teams of researchers to develop new therapies in a cost-effective manner and get them to patients quickly.

Virtual biotechs, using innovative research and clinical and business strategies, will exploit the Commons to slash the time and costs associated with drug development. As a result, a small group of patients with a rare disease can fund the development of new therapy in time to save their own lives. In the age of genomic sub-typing and personalized medicine–where every disease is potentially an orphan disease–the implications are far-reaching.

Abstract: We have a rare moment in time where we can change the entire system in one motion by establishing a collaborative ecosystem of knowledge and research services that can be rapidly assembled to develop new therapies with unprecedented efficiencies and economies of scale. We can create the same radical increase in efficiency for scientific research that commerce saw in the 1990s, as secure Internet transactions transformed many vertically integrated industries into horizontally integrated ecosystems of service providers and consumers. The explosion of contract vendors in biotechnology, covering the spectrum from gene to protein to drug discovery, development and trials, is one factor. The emergence of the Semantic Web for science is part of the story, as is the existence proof that common use licensing can create explosive value in software and culture. And the power of the network to bring these elements together into a unified system, a Health Commons, is the final piece of the puzzle.

Abstract: Within BioBike, a Web-based biocomputing environment, the BBL language enables biologists to program novel genomic algorithms. To engage users without prior programming experience, we have implemented a visual programming environment that allows users to create BBL code through a dynamic, direct-manipulation interface. The environment provides those new to programming with the ability to make use of a full-featured programming language, even to the point of easily building new functionality into BioBike.

Abstract: An approach to question answering through automated deduction is advocated. Answers to questions are extracted from proofs of associated conjectures over an axiomatic theory of the subject domain. External knowledge resources, including data and software, are consulted through a mechanism known as procedural attachment. A researcher ignorant of the subject domain theory or its logical language can formulate questions via a query elicitation facility. A similar device allows an expert to extend the theory. An English explanation for each answer, and a justification for its correctness, is constructed automatically from the proof by which it was extracted. A deductive approach has been applied in planetary astronomy, geography, intelligence analysis, and, most recently, molecular biology and medical research applications. It is argued that the constructs in the Semantic Web languages, including OWL with SWRL, are insufficiently expressive for this kind of application.

Abstract: All of healthcare is an experiment, but we measure only a small portion of the outcomes, i.e., in clinical trials. We’re also not very good at collaboratively analyzing the data, interpreting the results, and disseminating them in a timely and meaningful manner. In this talk, CommerceNet Chairman and Founder Marty Tenenbaum presents a vision and technological approach for addressing these problems by using the Web to tap the collective intelligence of patients, physicians, and medical researchers, ultimately bringing the world’s knowledge and resources to bear on curing diseases one patient at a time.

Abstract:
Background
As biologists increasingly rely upon computational tools, it is imperative that they be able to appropriately apply these tools and clearly understand the methods the tools employ. Such tools must have access to all the relevant data and knowledge and, in some sense, “understand” biology so that they can serve biologists’ goals appropriately and “explain” in biological terms how results are computed.

Methodology/Principal Findings
We describe a deduction-based approach to biocomputation that semi-automatically combines knowledge, software, and data to satisfy goals expressed in a high-level biological language. The approach is implemented in an open source Web-based biocomputing platform called BioDeducta, which combines SRI’s SNARK theorem prover with the BioBike interactive integrated knowledge base. The biologist/user expresses a high-level conjecture, representing a biocomputational goal query, without indicating how this goal is to be achieved. A subject domain theory, represented in SNARK’s logical language, transforms the terms in the conjecture into capabilities of the available resources and the background knowledge necessary to link them together. If the subject domain theory enables SNARK to prove the conjecture—that is, to find paths between the goal and BioBike resources—then the resulting proofs represent solutions to the conjecture/query. Such proofs provide provenance for each result, indicating in detail how they were computed. We demonstrate BioDeducta by showing how it can approximately replicate a previously published analysis of genes involved in the adaptation of cyanobacteria to different light niches.

Conclusions/Significance
Through the use of automated deduction guided by a biological subject domain theory, this work is a step towards enabling biologists to conveniently and efficiently marshal integrated knowledge, data, and computational tools toward resolving complex biological queries.

Abstract: Programming languages are, at the same time, instruments and communicative artifacts that evolve rapidly through use. This paper describes an online computing platform called BioBike. BioBike is a trading zone where biologists and programmers collaborate in the development of an extended vocabulary and functionality for computational genomics. In the course of this work, they develop interactional expertise with one another’s domains. The extended BioBike vocabulary operates on two planes: as a working programming language, and as a pidgin in the conversation between the biologists and engineers. The flexibility that permits this community to dynamically extend BioBike’s working vocabulary—to form new pidgins—makes BioBike unique among computational tools, which usually are not themselves adapted through the collaborations that they facilitate. Thus BioBike is itself a crucial feature—which it is tempting to refer to as a participant—in the developing interaction.

Abstract: This paper explores the application of e-commerce technologies to healthcare and concludes that there is indeed a substantial opportunity to leverage both experience and technology from industry. We begin by discussing the major integration challenges encountered in e-business, and how they are being addressed through e-marketplaces and business service networks (BSNs). We then outline the design for a healthcare BSN for the Silicon Valley, and discuss implementation and adoption issues. We conclude by speculating about the implications of this approach for other Regional Health Information Organizations (RHIOs), the National Health Information Infrastructure (NHII), and the future of healthcare.