Do peers see more in a paper than its authors?

Recent years have shown a gradual shift in the content of biomedical publications that is freely accessible, from titles and abstracts to full text. This has enabled new forms of automatic text analysis and has given rise to some interesting questions: How informative is the abstract compared to the full-text? What important information in the full-text is not present in the abstract? What should a good summary contain that is not already in the abstract? Do authors and peers see an article differently? We answer these questions by comparing the information content of the abstract to that in citances-sentences containing citations to that article. We contrast the important points of an article as judged by its authors versus as seen by peers. Focusing on the area of molecular interactions, we perform manual and automatic analysis, and we find that the set of all citances to a target article not only covers most information (entities, functions, experimental methods, and other biological concepts) found in its abstract, but also contains 20% more concepts. We further present a detailed summary of the differences across information types, and we examine the effects other citations and time have on the content of citances

Using Text Surrounding Method to Enhance Retrieval of Online Images by Google Search Engine

Purpose: the current research aimed to compare the effectiveness of various tags and codes for retrieving images from the Google. Design/methodology: selected images with different characteristics in a registered domain were carefully studied. The exception was that special conceptual features have been apportioned for each group of images separately. In this regard, each image group surrounding texts was dissimilar. Images were allocated with captionsincluding language in Farsi and English, alt text, image title, file name, free and controlled languages and appropriation text to images properties. Findings: allocating texts to images on a website causes Google to retrieve more images. Chi-square test for identification of significant differences among retrieved images in 5 Codes and revealed that in different codes, various numbers of images that were retrieved were significantly different. Caption allocation in English proved to have the best effect in retrieving images in the study sample, whereas file name had less effect in image retrieval ranking. Results of the Kruskal-Wallis test to assess the group differences in 5 codes revealed that differences were significant. Originality/Value: This paper tries to recall the importance of some elements which a search engine like Google may consider in indexing and retrieval of images. Widespread use of image tagging on the web enables Google and also other search engines to successfully retrieve images

Fast, linked, and open – the future of taxonomic publishing for plants: launching the journal PhytoKeys

The paper describes the focus, scope and the rationale of PhytoKeys, a newly established, peer-reviewed, open-access journal in plant systematics. PhytoKeys is launched to respond to four main challenges of our time: (1) Appearance of electronic publications as amendments or even alternatives to paper publications; (2) Open Access (OA) as a new publishing model; (3) Linkage of electronic registers, indices and aggregators that summarize information on biological species through taxonomic names or their persistent identifiers (Globally Unique Identifiers or GUIDs; currently Life Science Identifiers or LSIDs); (4) Web 2.0 technologies that permit the semantic markup of, and semantic enhancements to, published biological texts. The journal will pursue cutting-edge technologies in publication and dissemination of biodiversity information while strictly following the requirements of the current International Code of Botanical Nomenclature (ICBN)

Shadows : uma nova forma de representar documentos

Orientador: Claudia Maria Bauzer MedeirosDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Ferramentas de produção de documentos estão cada vez mais acessíveis e sofisticadas, resultando em um crescimento exponencial de documentos cada vez mais complexos, distribuídos e heterogêneos. Isto dificulta os processos de troca, anotação e recuperação de documentos. Enquanto mecanismos de recuperação da informação concentram-se apenas no processamento de características textuais (análise de corpus), estratégias de anotação de documentos procuram concentrar-se em formatos específicos ou exigem que o documento a ser anotado siga padrões de interoperabilidade - definidos por esquemas. Este trabalho apresenta o nosso esforço para lidar com estes problemas, propondo uma solução mais flexível para estes e outros processos. Ao invés de tentar modificar ou converter um documento, ou concentrar-se apenas nas características textuais deste, a estratégia descrita nesta dissertação propõe a elaboração de um descritor intermediário - denominado shadow - que representa e sumariza aspectos e elementos da estrutura e do conteúdo de um documento que sejam relevantes a um dado domínio. Shadows não se restringem à descrição de características textuais de um documento, preservando, por exemplo, a hierarquia entre os elementos e descrevendo outros tipos de artefatos, como artefatos multimídia. Além disto, Shadows podem ser anotados e armazenados em bancos de dados, permitindo consultas sobre a estrutura e conteúdo de documentos, independentemente de formatosAbstract: Document production tools are present everywhere, resulting in an exponential growth of increasingly complex, distributed and heterogeneous documents. This hampers document exchange, as well as their annotation and retrieval. While information retrieval mechanisms concentrate on textual features (corpus analysis), annotation approaches either target specific formats or require that a document follows interoperable standards - defined via schemas. This work presents our effort to handle these problems, providing a more flexible solution. Rather than trying to modify or convert the document itself, or to target only textual characteristics, the strategy described in this work is based on an intermediate descriptor - the document shadow. A shadow represents domain-relevant aspects and elements of both structure and content of a given document. Shadows are not restricted to the description of textual features, but also concern other elements, such as multimedia artifacts. Furthermore, shadows can be stored in a database, thereby supporting queries on document structure and content, regardless document formatsMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research

A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase

A game-based approach towards human augmented image annotation.

PhDImage annotation is a difficult task to achieve in an automated way. In this thesis, a human-augmented approach to tackle this problem is discussed and suitable strategies are derived to solve it. The proposed technique is inspired by human-based computation in what is called “human-augmented” processing to overcome limitations of fully automated technology for closing the semantic gap. The approach aims to exploit what millions of individual gamers are keen to do, i.e. enjoy computer games, while annotating media. In this thesis, the image annotation problem is tackled by a game based framework. This approach combines image processing and a game theoretic model to gather media annotations. Although the proposed model behaves similar to a single player game model, the underlying approach has been designed based on a two-player model which exploits the player’s contribution to the game and previously recorded players to improve annotations accuracy. In addition, the proposed framework is designed to predict the player’s intention through Markovian and Sequential Sampling inferences in order to detect cheating and improve annotation performances. Finally, the proposed techniques are comprehensively evaluated with three different image datasets and selected representative results are reported