Broadening the Scope of Nanopublications

In this paper, we present an approach for extending the existing concept of nanopublications --- tiny entities of scientific results in RDF representation --- to broaden their application range. The proposed extension uses English sentences to represent informal and underspecified scientific claims. These sentences follow a syntactic and semantic scheme that we call AIDA (Atomic, Independent, Declarative, Absolute), which provides a uniform and succinct representation of scientific assertions. Such AIDA nanopublications are compatible with the existing nanopublication concept and enjoy most of its advantages such as information sharing, interlinking of scientific findings, and detailed attribution, while being more flexible and applicable to a much wider range of scientific results. We show that users are able to create AIDA sentences for given scientific results quickly and at high quality, and that it is feasible to automatically extract and interlink AIDA nanopublications from existing unstructured data sources. To demonstrate our approach, a web-based interface is introduced, which also exemplifies the use of nanopublications for non-scientific content, including meta-nanopublications that describe other nanopublications.Comment: To appear in the Proceedings of the 10th Extended Semantic Web Conference (ESWC 2013

Towards Customizable Chart Visualizations of Tabular Data Using Knowledge Graphs

Scientific articles are typically published as PDF documents, thus rendering the extraction and analysis of results a cumbersome, error-prone, and often manual effort. New initiatives, such as ORKG, focus on transforming the content and results of scientific articles into structured, machine-readable representations using Semantic Web technologies. In this article, we focus on tabular data of scientific articles, which provide an organized and compressed representation of information. However, chart visualizations can additionally facilitate their comprehension. We present an approach that employs a human-in-the-loop paradigm during the data acquisition phase to define additional semantics for tabular data. The additional semantics guide the creation of chart visualizations for meaningful representations of tabular data. Our approach organizes tabular data into different information groups which are analyzed for the selection of suitable visualizations. The set of suitable visualizations serves as a user-driven selection of visual representations. Additionally, customization for visual representations provides the means for facilitating the understanding and sense-making of information

Provenance-Centered Dataset of Drug-Drug Interactions

Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank), etc. While each one of these approaches is properly statistically validated, they do not take into consideration the overlap between them as one of their decision making variables. In this paper we present LInked Drug-Drug Interactions (LIDDI), a public nanopublication-based RDF dataset with trusty URIs that encompasses some of the most cited prediction methods and sources to provide researchers a resource for leveraging the work of others into their prediction methods. As one of the main issues to overcome the usage of external resources is their mappings between drug names and identifiers used, we also provide the set of mappings we curated to be able to compare the multiple sources we aggregate in our dataset.Comment: In Proceedings of the 14th International Semantic Web Conference (ISWC) 201

Tools and collaborative environments for bioinformatics research

Advanced research requires intensive interaction among a multitude of actors, often possessing different expertise and usually working at a distance from each other. The field of collaborative research aims to establish suitable models and technologies to properly support these interactions. In this article, we first present the reasons for an interest of Bioinformatics in this context by also suggesting some research domains that could benefit from collaborative research. We then review the principles and some of the most relevant applications of social networking, with a special attention to networks supporting scientific collaboration, by also highlighting some critical issues, such as identification of users and standardization of formats. We then introduce some systems for collaborative document creation, including wiki systems and tools for ontology development, and review some of the most interesting biological wikis. We also review the principles of Collaborative Development Environments for software and show some examples in Bioinformatics. Finally, we present the principles and some examples of Learning Management Systems. In conclusion, we try to devise some of the goals to be achieved in the short term for the exploitation of these technologies

Comparison of Goal-Directed Hemodynamic Optimization Using Pulmonary Artery Catheter and Transpulmonary Thermodilution in Combined Valve Repair: A Randomized Clinical Trial

Our aim was to compare the effects of goal-directed therapy guided either by pulmonary artery catheter (PAC) or by transpulmonary thermodilution (TTD) combined with monitoring of oxygen transport on perioperative hemodynamics and outcome after complex elective valve surgery. Measurements and Main Results. Forty patients were randomized into two equal groups: a PAC group and a TTD group. In the PAC group, therapy was guided by mean arterial pressure (MAP), cardiac index (CI) and pulmonary artery occlusion pressure (PAOP), whereas in the TTD group we additionally used global end-diastolic volume index (GEDVI), extravascular lung water index (EVLWI), and oxygen delivery index (DO2I). We observed a gradual increase in GEDVI, whereas EVLWI and PAOP decreased by 20–30% postoperatively (P < 0.05). The TTD group received 20% more fluid accompanied by increased stroke volume index and DO2I by 15–20% compared to the PAC group (P < 0.05). Duration of mechanical ventilation was increased by 5.2 hrs in the PAC group (P = 0.04). Conclusions. As compared to the PAC-guided algorithm, goal-directed therapy based on transpulmonary thermodilution and oxygen transport increases the volume of fluid therapy, improves hemodynamics and DO2I, and reduces the duration of respiratory support after complex valve surgery

Thesaurus-based disambiguation of gene symbols

BACKGROUND: Massive text mining of the biological literature holds great promise of relating disparate information and discovering new knowledge. However, disambiguation of gene symbols is a major bottleneck. RESULTS: We developed a simple thesaurus-based disambiguation algorithm that can operate with very little training data. The thesaurus comprises the information from five human genetic databases and MeSH. The extent of the homonym problem for human gene symbols is shown to be substantial (33% of the genes in our combined thesaurus had one or more ambiguous symbols), not only because one symbol can refer to multiple genes, but also because a gene symbol can have many non-gene meanings. A test set of 52,529 Medline abstracts, containing 690 ambiguous human gene symbols taken from OMIM, was automatically generated. Overall accuracy of the disambiguation algorithm was up to 92.7% on the test set. CONCLUSION: The ambiguity of human gene symbols is substantial, not only because one symbol may denote multiple genes but particularly because many symbols have other, non-gene meanings. The proposed disambiguation approach resolves most ambiguities in our test set with high accuracy, including the important gene/not a gene decisions. The algorithm is fast and scalable, enabling gene-symbol disambiguation in massive text mining applications

Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease

Background: Comparative analysis of expression microarray studies is difficult due to the large influence of technical factors on experimental outcome. Still, the identified differentially expressed genes may hint at the same biological processes. However, manually curated assignment of genes to biological processes, such as pursued by the Gene Ontology (GO) consortium, is incomplete and limited. We hypothesised that automatic association of genes with biological processes through thesaurus-controlled mining of Medline abstracts would be more effective. Therefore, we developed a novel algorithm (LAMA: Literature-Aided Meta-Analysis) to quantify the similarity between transcriptomics studies. We evaluated our algorithm on a large compendium of 102 microarray studies published in the field of muscle development and disease, and compared it to similarity measures based on gene overlap and over-representation of biological processes assigned by GO. Results: While the overlap in both genes and overrepresented GO-terms was poor, LAMA retrieved many more biologically meaningful links between studies, with substantially lower influence of technical factors. LAMA correctly grouped muscular dystrophy, regeneration and myositis studies, and linked patient and corresponding mouse model studies. LAMA also retrieves the connecting biological concepts. Among other new discoveries, we associated cullin proteins, a class of ubiquitinylation proteins, with genes down-regulated during muscle regeneration, whereas ubiquitinylation was previously reported to be activated during the inverse process: muscle atrophy. Conclusion: Our literature-based association analysis is capable of finding hidden common biological denominators in microarray studies, and circumvents the need for raw data analysis or curated gene annotation databases

Social norms for e-cigarettes and smoking: associations with initiation of e-cigarette use, intentions to quit smoking and quit attempts: findings from the EUREST-PLUS ITC Europe Surveys

Background: Social norms have received little attention in relation to electronic cigarettes (EC). The current study examine social norms for EC use and smoking tobacco, and their associations with (i) initiation of EC use, (ii) intention to quit smoking and (iii) attempts to quit smoking. Methods: Cross-sectional and longitudinal data analysis from Waves 1 and 2 of the ITC 6 European Country Survey and corresponding waves from England (the ITC Four Country Smoking and Vaping Survey). Current smokers at baseline, who heard of ECs and provided data at both waves were included (n = 3702). Complex samples logistic regression examined associations between the outcomes and descriptive (seeing EC use in public, close friends using ECs/smoking) and injunctive (public approves of ECs/smoking) norms, adjusting for country, demographics, EC use and heaviness of smoking. Results: In longitudinal analyses, seeing EC use in public at least some days was the only social norm that predicted initiation of EC use between waves (OR = 1.66, 95%CI = 1.08–2.56). In the cross-sectional analysis, having an intention to quit was associated with seeing EC use in public (OR = 1.37, 95%CI = 1.04–1.81) and reporting fewer than three close friends smoke (OR = 0.59, 95%CI = 0.44–0.80). There was no association between any social norm and making a quit attempt between waves. Conclusions: Initiation of EC use is predicted by seeing EC use in public, which was also associated with greater intention to quit smoking. Friends’ smoking was associated with lower intention to quit. These findings may allay concerns that increased visibility of ECs is renormalizing smoking amongst current smokers

Education achievement and type 2 diabetes-what mediates the relationship in older adults? Data from the ESTHER study : a population-based cohort study

Peer reviewe

Knowledge sharing and collaboration in translational research, and the DC-THERA Directory

Biomedical research relies increasingly on large collections of data sets and knowledge whose generation, representation and analysis often require large collaborative and interdisciplinary efforts. This dimension of ‘big data’ research calls for the development of computational tools to manage such a vast amount of data, as well as tools that can improve communication and access to information from collaborating researchers and from the wider community. Whenever research projects have a defined temporal scope, an additional issue of data management arises, namely how the knowledge generated within the project can be made available beyond its boundaries and life-time. DC-THERA is a European ‘Network of Excellence’ (NoE) that spawned a very large collaborative and interdisciplinary research community, focusing on the development of novel immunotherapies derived from fundamental research in dendritic cell immunobiology. In this article we introduce the DC-THERA Directory, which is an information system designed to support knowledge management for this research community and beyond. We present how the use of metadata and Semantic Web technologies can effectively help to organize the knowledge generated by modern collaborative research, how these technologies can enable effective data management solutions during and beyond the project lifecycle, and how resources such as the DC-THERA Directory fit into the larger context of e-science