Calling on a million minds for community annotation in WikiProteins.

WikiProteins enables community annotation in a Wiki-based system. Extracts of major data sources have been fused into an editable environment that links out to the original sources. Data from community edits create automatic copies of the original data. Semantic technology captures concepts co-occurring in one sentence and thus potential factual statements. In addition, indirect associations between concepts have been calculated. We call on a 'million minds' to annotate a 'million concepts' and to collect facts from the literature with the reward of collaborative knowledge discovery. The system is available for beta testing at http://www.wikiprofessional.org.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Assignment of protein function and discovery of novel nucleolar proteins based on automatic analysis of MEDLINE.

Attribution of the most probable functions to proteins identified by proteomics is a significant challenge that requires extensive literature analysis. We have developed a system for automated prediction of implicit and explicit biologically meaningful functions for a proteomics study of the nucleolus. This approach uses a set of vocabulary terms to map and integrate the information from the entire MEDLINE database. Based on a combination of cross-species sequence homology searches and the corresponding literature, our approach facilitated the direct association between sequence data and information from biological texts describing function. Comparison of our automated functional assignment to manual annotation demonstrated our method to be highly effective. To establish the sensitivity, we defined the functional subtleties within a family containing a highly conserved sequence. Clustering of the DEAD-box protein family of RNA helicases confirmed that these proteins shared similar morphology although functional subfamilies were accurately identified by our approach. We visualized the nucleolar proteome in terms of protein functions using multi-dimensional scaling, showing functional associations between nucleolar proteins that were not previously realized. Finally, by clustering the functional properties of the established nucleolar proteins, we predicted novel nucleolar proteins. Subsequently, nonproteomics studies confirmed the predictions of previously unidentified nucleolar proteins

An Online Ontology: WiktionaryZ

There is a great demand for online maintenance and refinement of knowledge on biomedical entities 1. Collaborative maintenance of large biomedical ontologies combines the intellectual capacity of millions of minds for updating and correcting the annotations of biomedical concepts with their semantic relationships according to latest scientific insights. These relationships extend the current specialization and participation relationships as currently exploited in most ontology projects. The ontology layer has been developed on top of the Wikidata 2 component and allows for presentation of these biomedical concepts in a similar way as Wikipedia pages. Each page contains all information on a biomedical concept with semantic relationships to other related concepts. A first version has been populated with data from the Unified Medical Language System (UMLS), SwissProt, GeneOntology, and Gemet. The various fields are online editable in a Wiki style and are maintained via a powerful versioning regiment. Next steps will include the definition of a set of formal rules for the ontology to enforce (onto)logical rigor

Why was this transfusion given? : Identifying clinical indications for blood transfusion in health care data

Background: To enhance the utility of transfusion data for research, ideally every transfusion should be linked to a primary clinical indication. In electronic patient records, many diagnostic and procedural codes are registered, but unfortunately, it is usually not specified which one is the reason for transfusion. Therefore, a method is needed to determine the most likely indication for transfusion in an automated way. Study design and methods: An algorithm to identify the most likely transfusion indication was developed and evaluated against a gold standard based on the review of medical records for 234 cases by 2 experts. In a second step, information on misclassification was used to fine-tune the initial algorithm. The adapted algorithm predicts, out of all data available, the most likely indication for transfusion using information on medical specialism, surgical procedures, and diagnosis and procedure dates relative to the transfusion date. Results: The adapted algorithm was able to predict 74.4% of indications in the sample correctly (extrapolated to the full data set 75.5%). A kappa score, which corrects for the number of options to choose from, was found of 0.63. This indicates that the algorithm performs substantially better than chance level. Conclusion: It is possible to use an automated algorithm to predict the indication for transfusion in terms of procedures and/or diagnoses. Before implementation of the algorithm in other data sets, the obtained results should be externally validated in an independent hospital data set

Validation of multisource electronic health record data: an application to blood transfusion data

Abstract Background Although data from electronic health records (EHR) are often used for research purposes, systematic validation of these data prior to their use is not standard practice. Existing validation frameworks discuss validity concepts without translating these into practical implementation steps or addressing the potential influence of linking multiple sources. Therefore we developed a practical approach for validating routinely collected data from multiple sources and to apply it to a blood transfusion data warehouse to evaluate the usability in practice. Methods The approach consists of identifying existing validation frameworks for EHR data or linked data, selecting validity concepts from these frameworks and establishing quantifiable validity outcomes for each concept. The approach distinguishes external validation concepts (e.g. concordance with external reports, previous literature and expert feedback) and internal consistency concepts which use expected associations within the dataset itself (e.g. completeness, uniformity and plausibility). In an example case, the selected concepts were applied to a transfusion dataset and specified in more detail. Results Application of the approach to a transfusion dataset resulted in a structured overview of data validity aspects. This allowed improvement of these aspects through further processing of the data and in some cases adjustment of the data extraction. For example, the proportion of transfused products that could not be linked to the corresponding issued products initially was 2.2% but could be improved by adjusting data extraction criteria to 0.17%. Conclusions This stepwise approach for validating linked multisource data provides a basis for evaluating data quality and enhancing interpretation. When the process of data validation is adopted more broadly, this contributes to increased transparency and greater reliability of research based on routinely collected electronic health records

Validation of multisource electronic health record data : An application to blood transfusion data

Background: Although data from electronic health records (EHR) are often used for research purposes, systematic validation of these data prior to their use is not standard practice. Existing validation frameworks discuss validity concepts without translating these into practical implementation steps or addressing the potential influence of linking multiple sources. Therefore we developed a practical approach for validating routinely collected data from multiple sources and to apply it to a blood transfusion data warehouse to evaluate the usability in practice. Methods: The approach consists of identifying existing validation frameworks for EHR data or linked data, selecting validity concepts from these frameworks and establishing quantifiable validity outcomes for each concept. The approach distinguishes external validation concepts (e.g. concordance with external reports, previous literature and expert feedback) and internal consistency concepts which use expected associations within the dataset itself (e.g. completeness, uniformity and plausibility). In an example case, the selected concepts were applied to a transfusion dataset and specified in more detail. Results: Application of the approach to a transfusion dataset resulted in a structured overview of data validity aspects. This allowed improvement of these aspects through further processing of the data and in some cases adjustment of the data extraction. For example, the proportion of transfused products that could not be linked to the corresponding issued products initially was 2.2% but could be improved by adjusting data extraction criteria to 0.17%. Conclusions: This stepwise approach for validating linked multisource data provides a basis for evaluating data quality and enhancing interpretation. When the process of data validation is adopted more broadly, this contributes to increased transparency and greater reliability of research based on routinely collected electronic health records

Additional file 1: Table S1. of Validation of multisource electronic health record data: an application to blood transfusion data

Occurrence of data quality concepts in existing EHR quality assessment or data linkage frameworks. Table S2.1. Distribution of number of pending diagnoses per transfusion. Figure S2.1. Time patterns in number of donations, products and donors. Figure S2.2. Time patterns in number of transfusions by product type. Figure S2.3. Time patterns in % of transfusion that initially could not be linked to products issued. Figure S2.4. Comparison with previous literature: Distribution of blood products over age and gender, by product type. Table S3. Data validity outcomes reported in the literature for studies that use transfusion databases. Text. Similarities with other operationalizations of data validity. (DOCX 205 kb

Effects of dexamethasone on cognitive decline after cardiac surgery a randomized clinical trial

Background: Cardiac surgery can be complicated by postoperative cognitive decline (POCD), which is characterized by impaired memory function and intellectual ability. The systemic inflammatory response that is induced by major surgery and cardiopulmonary bypass may play an important role in the etiology of POCD. Prophylactic corticosteroids to attenuate the inflammatory response may therefore reduce the risk of POCD. The authors investigated the effect of intraoperative high-dose dexamethasone on the incidence of POCD at 1 month and 12 months after cardiac surgery. Methods: This multicenter, randomized, double-blind, placebo-controlled trial is a preplanned substudy of the DExamethasone for Cardiac Surgery trial. A total of 291 adult patients undergoing cardiac surgery with cardiopulmonary bypass were recruited in three hospitals and randomized to receive dexamethasone 1 mg/kg (n = 145) or placebo (n = 146). The main outcome measures were incidence of POCD at 1- And 12-month follow-up, defined as a decline in neuropsychological test performance beyond natural variability, as measured in a control group. Results: At 1-month follow-up, 19 of 140 patients in the dexamethasone group (13.6%) and 10 of 138 patients in the placebo group (7.2%) fulfilled the diagnostic criteria for POCD (relative risk, 1.87; 95% CI, 0.90 to 3.88; P = 0.09). At 12-month follow-up, 8 of 115 patients in the dexamethasone group (7.0%) and 4 of 114 patients (3.5%) in the placebo group had POCD (relative risk, 1.98; 95% CI, 0.61 to 6.40; P = 0.24). Conclusion: Intraoperative high-dose dexamethasone did not reduce the risk of POCD after cardiac surgery