The ongoing debate on secondary use of health data for research has been renewed by the passage of comprehensive data privacy laws that shift control from institutions back to the individuals on whom the data was collected. Rights-based data privacy laws, while lauded by individuals, are viewed as problematic for the researcher due to the distributed nature of data control. Efforts such as the European Health Data Space initiative seek to build a new mechanism for secondary use that erodes individual control in favor of broader secondary use for beneficial health research. Health information sharing platforms do exist that embrace rights-based data privacy while simultaneously providing a rich research environment for secondary data use. The benefits of embracing rights-based data privacy to promote transparency of data use along with control of one’s participation builds the trust necessary for more inclusive/diverse/representative clinical research.

The transition to open data practices is straightforward albeit surprisingly challenging to implement largely due to cultural and policy issues. A general data sharing framework is presented along with two case studies that highlight these challenges and offer practical solutions that can be adjusted depending on the type of data collected, the country in which the study is initiated, and the prevailing research culture. Embracing the constraints imposed by data privacy considerations, especially for biomedical data, must be emphasized for data outside of the United States until data privacy law(s) are established at the Federal and/or State level.

Drawing on a landscape analysis of existing data-sharing initiatives, in-depth interviews with expert stakeholders, and public deliberations with community advisory panels across the U.S., we describe features of the evolving medical information commons (MIC). We identify participant-centricity and trustworthiness as the most important features of an MIC and discuss the implications for those seeking to create a sustainable, useful, and widely available collection of linked resources for research and other purposes.

The ability to rapidly and reliably develop hypotheses on the function of newly discovered protein sequences requires systematic and comprehensive analysis. Such an analysis, embodied within the DS GeneAtlas™ pipeline, has been used to critically evaluate the severe acute respiratory syndrome (SARS) genome with the goal of identifying new potential targets for viral therapeutic intervention. This paper discusses several new functional hypotheses on the roles played by the constituent gene products of SARS, and will serve as an example of how such assignments can be developed or extended on other systems of interest.

Rapidly and reliably developing hypotheses on the function of newly discovered protein sequences requires systematic and comprehensive analysis. Such an analysis, embodied within the Discovery Studio® GeneAtlasTM pipeline, has been used to critically evaluate the SARS genome with the goal of identifying new potential targets for therapeutic intervention. This study discusses several new functional hypotheses on the roles played by the constituent gene products of SARS, and serves as an example of how such assignments can be developed or extended on other systems of interest. The goal of this study is to identify new potential targets for SARS therapeutic intervention.

We present the Cerius 2 Structure‐Based Focusing (SBF) application. This application was applied to the estrogen receptor. A series of three‐dimensional queries were generated for the binding site of the receptor. The queries consist of combinations of hydrogen bond donors and acceptors, and lipophilic features for the binding site along with excluded volume regions occupied by the receptor atoms. A database of 31 ligands with known relative binding affinities for the estrogen receptor was used to examine the selectivity of the queries. The objective of the study was to determine if queries generated by the Cerius 2 SBF method could differentiate between the more and less active ligands of the training set. Results are promising, with the generated queries showing greater selectivity toward the more active ligands. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 993–1003, 2001

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTComputer-Assisted Structure Determination. Structure of the Peptide Moroidin from Laportea moroidesS. D. Kahn, P. M. Booth, J. P. Waltho, and D. H. WilliamsCite this: J. Org. Chem. 2000, 65, 24, 8406Publication Date (Web):November 8, 2000Publication History Published online8 November 2000Published inissue 1 December 2000https://pubs.acs.org/doi/10.1021/jo004021nhttps://doi.org/10.1021/jo004021ncorrectionACS PublicationsCopyright © 2000 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views410Altmetric-Citations13LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (11 KB) Get e-Alertsclose Get e-Alerts

Structure-activity relationship studies of nucleoside transport inhibitors have revealed a diverse group of compounds with potent inhibitory activity against the major mammalian equilibrative nucleoside transporter, the es transporter. Inhibitors of the es transporter have potential therapeutic applications for adenosine potentiation in heart disease and stroke, as well as for anticancer and antiviral chemotherapy. Computational techniques have been applied to derive a pharmacophore hypothesis of generalized chemical interaction features that can be used to search 3D molecular databases to identify novel inhibitors. The methodology of feature-based hypothesis generation and the results are presented .