Outcome Prediction for Unipolar Depression

Although effective drug and non-drug treatment for unipolar depressive illness exist, different individuals respond differently to different treatments. It is not uncommon for a given patient to lw switched several times from one treatment to another until an effective remedy for that particular patient is found. This process is costly in terms of time, money and suffering. It is thus desirable to determine at the outset the likdy response of a patient to the available treatments, so that the optimal one can be selected. Although prior attempts at outcome prediction with linear regression models have failed, recent work on this problem has indicated that the nonlinear predictive techniques of backpropagation and quadratic regression call account for a significant proportion of the variance in the data. The present research applies the nonlinear predictive technique of kernel regression to this problcrn, and employs cross-validation to test the ability of the resulting model to extract, from extremely noisy dinical data, information with predictive value. The importance of comparison with a suitable null hypothesis is illustrated.Office of Naval Research (N00014-95-1-0409

A linked data representation for summary statistics and grouping criteria

Summary statistics are fundamental to data science, and are the buidling blocks of statistical reasoning. Most of the data and statistics made available on government web sites are aggregate, however, until now, we have not had a suitable linked data representation available. We propose a way to express summary statistics across aggregate groups as linked data using Web Ontology Language (OWL) Class based sets, where members of the set contribute to the overall aggregate value. Additionally, many clinical studies in the biomedical field rely on demographic summaries of their study cohorts and the patients assigned to each arm. While most data query languages, including SPARQL, allow for computation of summary statistics, they do not provide a way to integrate those values back into the RDF graphs they were computed from. We represent this knowledge, that would otherwise be lost, through the use of OWL 2 punning semantics, the expression of aggregate grouping criteria as OWL classes with variables, and constructs from the Semanticscience Integrated Ontology (SIO), and the World Wide Web Consortium’s provenance ontology, PROV-O, providing interoperable representations that are well supported across the web of Linked Data. We evaluate these semantics using a Resource Description Framework (RDF) representation of patient case information from the Genomic Data Commons, a data portal from the National Cancer Institute

Integrating findings of traditional medicine with modern pharmaceutical research: the potential role of linked open data

One of the biggest obstacles to progress in modern pharmaceutical research is the difficulty of integrating all available research findings into effective therapies for humans. Studies of traditionally used pharmacologically active plants and other substances in traditional medicines may be valuable sources of previously unknown compounds with therapeutic actions. However, the integration of findings from traditional medicines can be fraught with difficulties and misunderstandings. This article proposes an approach to use linked open data and Semantic Web technologies to address the heterogeneous data integration problem. The approach is based on our initial experiences with implementing an integrated web of data for a selected use-case, i.e., the identification of plant species used in Chinese medicine that indicate potential antidepressant activities

Dynamic enhancement of drug product labels to support drug safety, efficacy, and effectiveness

Out-of-date or incomplete drug product labeling information may increase the risk of otherwise preventable adverse drug events. In recognition of these concerns, the United States Federal Drug Administration (FDA) requires drug product labels to include specific information. Unfortunately, several studies have found that drug product labeling fails to keep current with the scientific literature. We present a novel approach to addressing this issue. The primary goal of this novel approach is to better meet the information needs of persons who consult the drug product label for information on a drug’s efficacy, effectiveness, and safety. Using FDA product label regulations as a guide, the approach links drug claims present in drug information sources available on the Semantic Web with specific product label sections. Here we report on pilot work that establishes the baseline performance characteristics of a proof-of-concept system implementing the novel approach. Claims from three drug information sources were linked to the Clinical Studies, Drug Interactions, and Clinical Pharmacology sections of the labels for drug products that contain one of 29 psychotropic drugs. The resulting Linked Data set maps 409 efficacy/effectiveness study results, 784 drug-drug interactions, and 112 metabolic pathway assertions derived from three clinically-oriented drug information sources (ClinicalTrials.gov, the National Drug File – Reference Terminology, and the Drug Interaction Knowledge Base) to the sections of 1,102 product labels. Proof-of-concept web pages were created for all 1,102 drug product labels that demonstrate one possible approach to presenting information that dynamically enhances drug product labeling. We found that approximately one in five efficacy/effectiveness claims were relevant to the Clinical Studies section of a psychotropic drug product, with most relevant claims providing new information. We also identified several cases where all of the drug-drug interaction claims linked to the Drug Interactions section for a drug were potentially novel. The baseline performance characteristics of the proof-of-concept will enable further technical and user-centered research on robust methods for scaling the approach to the many thousands of product labels currently on the market

Full Information Product Pricing: An Information Strategy for Harnessing Consumer Choice to Create a More Sustainable World

Research and practice in the information systems (IS) field have been evolving over time, nourishing and promoting the development of applications that transform the relationships of individuals, corporations, and governments. Building on this evolution, we push forward a vision of the potential influence of the IS field into one of the most important problems of our times, an increasingly unsustainable world, which is traditionally considered the product of imperfect markets or market externalities. We describe our work in Full Information Product Pricing (FIPP) and our vision of a FIPP global socio-technical system, I-Choose, as a way to connect consumer choice and values with environmental, social, and economic effects of production and distribution practices. FIPP and I-Choose represent a vision about how information systems research can contribute to interdisciplinary research in supply chains, governance, and market economies to provide consumers with information packages that help them better understand how, where, and by whom the products they buy are produced. We believe that such a system will have important implications for international trade and agreements, for public policy, and for making a more sustainable world

A novel custom high density-comparative genomic hybridization array detects common rearrangements as well as deep intronic mutations in dystrophinopathies

<p>Abstract</p> <p>Background</p> <p>The commonest pathogenic <it>DMD </it>changes are intragenic deletions/duplications which make up to 78% of all cases and point mutations (roughly 20%) detectable through direct sequencing. The remaining mutations (about 2%) are thought to be pure intronic rearrangements/mutations or 5'-3' UTR changes. In order to screen the huge <it>DMD </it>gene for all types of copy number variation mutations we designed a novel custom high density comparative genomic hybridisation array which contains the full genomic region of the <it>DMD </it>gene and spans from 100 kb upstream to 100 kb downstream of the 2.2 Mb <it>DMD </it>gene.</p> <p>Results</p> <p>We studied 12 DMD/BMD patients who either had no detectable mutations or carried previously identified quantitative pathogenic changes in the <it>DMD </it>gene. We validated the array on patients with previously known mutations as well as unaffected controls, we identified three novel pure intronic rearrangements and we defined all the mutation breakpoints both in the introns and in the 3' UTR region. We also detected a novel polymorphic intron 2 deletion/duplication variation. Despite the high resolution of this approach, RNA studies were required to confirm the functional significance of the intronic mutations identified by CGH. In addition, RNA analysis identified three intronic pathogenic variations affecting splicing which had not been detected by the CGH analysis.</p> <p>Conclusion</p> <p>This novel technology represents an effective high throughput tool to identify both common and rarer DMD rearrangements. RNA studies are required in order to validate the significance of the CGH array findings. The combination of these tools will fully cover the identification of causative DMD rearrangements in both coding and non-coding regions, particularly in patients in whom standard although extensive techniques are unable to detect a mutation.</p

Building a Certification and Inspection Data Infrastructure to Promote Transparent Markets

This article reports on data architecture that reduces information asymmetries to support public-private collaboration to govern product certification and inspection for promoting transparent markets and building consumer trust. The data architecture is a proof-of-concept set of data standards called the Certification and Inspection Data Infrastructure Building Block (CIDIBB) for data storage, retrieval, sharing and automated reasoning of data that can be used to respond the question: what constitutes a trustworthy certification and inspection process? CIDIBB consists of three interrelated ontologies, focusing specifically on certified fair-trade coffee that has the potential to become universally applicable to any certification and inspection process for products or services. The evaluation results suggest that CIDIBB is able to test the trustworthiness of certification schemes, providing consistent results. CIDIBB will contribute to support public-private collaboration to solve public problems such as the promotion of sustainable production and fair labor practices

Introduction to semantic e-Science in biomedicine

The Semantic Web technologies provide enhanced capabilities that allow data and the meaning of the data to be shared and reused across application, enterprise, and community boundaries, better enabling integrative research and more effective knowledge discovery. This special issue is intended to give an introduction of the state-of-the-art of Semantic Web technologies and describe how such technologies would be used to build the e-Science infrastructure for biomedical communities. Six papers have been selected and included, featuring different approaches and experiences in a variety of biomedical domains