Full-text Scientific Articles Research Articles

NucleaRDB: information system for nuclear receptors

The NucleaRDB is a Molecular Class-Specific Information System that collects, combines, validates and disseminates large amounts of heterogeneous data on nuclear hormone receptors. It contains both experimental and computationally derived data. The data and knowledge present in the NucleaRDB can be accessed using a number of different interactive and programmatic methods and query systems. A nuclear hormone receptor-specific PDF reader interface is available that can integrate the contents of the NucleaRDB with full-text scientific articles. The NucleaRDB is freely available at http://www.receptors.org/nucleardb.

Condensing biomedical journal texts through paragraph ranking

The growing availability of full-text scientific articles raises the important issue of how to most efficiently digest full-text content. Although article titles and abstracts provide accurate and concise information on an article's contents, their brevity inevitably entails the loss of detail. Full-text articles provide those details, but require more time to read. The primary goal of this study is to combine the advantages of concise abstracts and detail-rich full-texts to ease the burden of reading. We retrieved abstract-related paragraphs from full-text articles through shared keywords between the abstract and paragraphs from the main text. Significant paragraphs were then recommended by applying a proposed paragraph ranking approach. Finally, the user was provided with a condensed text consisting of these significant paragraphs, allowing the user to save time from perusing the whole article. We compared the performance of the proposed approach with a keyword counting approach and a PageRank-like approach. Evaluation was conducted in two aspects: the importance of each retrieved paragraph and the information coverage of a set of retrieved paragraphs. In both evaluations, the proposed approach outperformed the other approaches. jchiang@mail.ncku.edu.tw.

DIGITAL TECHNOLOGIES IN THE SERVICE OF BIOLOGICAL SCIENCE

More than 90% of the world’s data has been created in the 2013-2014 years, and the pace is accelerating. Researchers have three main choices for building knowledgebases derived from the burgeoning literature: reading the articles (or abstracts) themselves, manual curation by experts, or using a specialized automated text-mining tool. 42% of highly cited papers appear in journals that are not traditionally highly cited. In 57% of cases, important information is mentioned only in the article itself and not in the abstract. Even the best human curators are not perfectly accurate. In one study using manual annotation of PubMed articles with Gene Ontology Annotation terms, only 39% of the terms assigned by three different curators were identical. In another study, the average precision of annotating medical events in clinical narratives by three experts was reported to be 88%. Between 2003-2012, more than one third of co-occurrences appeared in the body of an article prior to being published in abstracts. The current version of Elsevier’s text-mining technology has a 98% accuracy rate for entity detection and an 88% accuracy rate for relationship extraction. One of the most popular tools for the text mining and the presentation of results is Elsevier’s Pathway Studio. The number of articles researchers mentioned Pathway Studio is growing every year since 2003, reaching 170 articles in 2014. The number of citations of articles based on abstracts is also growing since 2004, reaching 153 citations in 2014. Also every year since 2006, a number of patents is growing, reaching 20 patents in 2014. The combination of content from top-quality journals, from Elsevier, coupled with proprietary automated text-mining technology that can process and extract critical information from literally millions of full-text scientific articles and tens of millions of abstracts in a matter of hours, provides a compelling competitive advantage for researcher's work.