PubTator central: automated concept annotation for biomedical full text articles.

This paper details the development and implementation of CiteGraph, a system for constructing large-scale citation and co-authorship networks from full-text biomedical articles. CiteGraph represents articles and authors by uniquely identified nodes, and connects those nodes through citation and co-authorship relations. CiteGraph network encompasses over 1.65 million full-text articles and 6.35 million citations by 1.37 million unique authors from the Elsevier full-text articles. Our evaluation shows 98% 99% F1-score for mapping a citation to the corresponding article and identifying MEDLINE articles. We further analyzed the characteristics of CiteGraph and found that they are consistent with assumptions made using small-scale bibliometric analysis. We also developed several novel network-based methods for analyzing publication, citation and collaboration patterns. This is the first work to develop a completely automated system for the creation of a large-scale citation network in the biomedical domain, and also to introduce novel findings in researcher publication histories. CiteGraph can be a useful resource to both the biomedical community, and bibliometric research.

Biomedical texts can be typically represented by four rhetorical categories: introduction, methods, results and discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied approaches to automatically classify sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We explored different approaches to automatically classify a sentence in a full-text biomedical article into the IMRAD categories. Our best system is a support vector machine classifier that achieved 81.30% accuracy, which is significantly higher than baseline systems.

Full-text articles, manuscript tracking, and automated literature search lead new array of AJKD online features.

RESTful Web service composition with BPEL for REST

Due to the recent development of Information Technology, the number of publications is increasing exponentially. In response to the increasing number of publications, there has been a sharp surge in the demand for replacing the existing manual text data processing by an automatic text data processing. Swanson proposed ABC model [1] on the top of text mining as a part of literature-based knowledge discovery for finding new possible biomedical hypotheses about three decades ago. The following clinical scholars proved the effectiveness of the possible hypotheses found by ABC model [2]. Such effectiveness let scholars try various literature-based knowledge discovery approaches [3, 4, 5]. However, their trials are not fully automated but hybrids of automatic and manual processes. The manual process requires the intervention of experts. In addition, their trials consider a single perspective. Even trials involving network theory have difficulties in mal-understanding the entire network structure of the relationships among concepts and the systematic interpretation on the structure [6, 7]. Thus, this study proposes a novel approach to discover various relationships by extending the intermediate concept B to a multi-leveled concept. By applying a graph-based path finding method based on co-occurrence and the relational entities among concepts, we attempt to systematically analyze and investigate the relationships between two concepts of a source node and a target node in the total paths. For the analysis of our study, we set our baseline as the result of Swanson [8]'s work. This work suggested the intermediate concept or terms between Raynaud's disease and fish oils as blood viscosity, platelet aggregability, and vasconstriction. We compared our results of intermediate concepts with these intermediate concepts of Swanson's. This study provides distinct perspectives for literature-based discovery by not only discovering the meaningful relationship among concepts in biomedical literature through graph-based path interference but also being able to generate feasible new hypotheses.

BackgroundWith the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE® abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: bm25 and the ranking algorithm implemented in the open-source Lucene search engine.ResultsExperiments show that treating an entire article as an indexing unit does not consistently yield higher effectiveness compared to abstract-only search. However, retrieval based on spans, or paragraphs-sized segments of full-text articles, consistently outperforms abstract-only search. Results suggest that highest overall effectiveness may be achieved by combining evidence from spans and full articles.ConclusionUsers searching full text are more likely to find relevant articles than searching only abstracts. This finding affirms the value of full text collections for text retrieval and provides a starting point for future work in exploring algorithms that take advantage of rapidly-growing digital archives. Experimental results also highlight the need to develop distributed text retrieval algorithms, since full-text articles are significantly longer than abstracts and may require the computational resources of multiple machines in a cluster. The MapReduce programming model provides a convenient framework for organizing such computations.

BackgroundComputational methods have been used to find duplicate biomedical publications in MEDLINE. Full text articles are becoming increasingly available, yet the similarities among them have not been systematically studied. Here, we quantitatively investigated the full text similarity of biomedical publications in PubMed Central.Methodology/Principal Findings72,011 full text articles from PubMed Central (PMC) were parsed to generate three different datasets: full texts, sections, and paragraphs. Text similarity comparisons were performed on these datasets using the text similarity algorithm eTBLAST. We measured the frequency of similar text pairs and compared it among different datasets. We found that high abstract similarity can be used to predict high full text similarity with a specificity of 20.1% (95% CI [17.3%, 23.1%]) and sensitivity of 99.999%. similarity and full text similarity have a moderate correlation (Pearson correlation coefficient: −0.423) when the similarity ratio is above 0.4. Among pairs of articles in PMC, method sections are found to be the most repetitive (frequency of similar pairs, methods: 0.029, introduction: 0.0076, results: 0.0043). In contrast, among a set of manually verified duplicate articles, results are the most repetitive sections (frequency of similar pairs, results: 0.94, methods: 0.89, introduction: 0.82). Repetition of introduction and methods sections is more likely to be committed by the same authors (odds of a highly similar pair having at least one shared author, introduction: 2.31, methods: 1.83, results: 1.03). There is also significantly more similarity in pairs of review articles than in pairs containing one review and one nonreview paper (frequency of similar pairs: 0.0167 and 0.0023, respectively).Conclusion/SignificanceWhile quantifying abstract similarity is an effective approach for finding duplicate citations, a comprehensive full text analysis is necessary to uncover all potential duplicate citations in the scientific literature and is helpful when establishing ethical guidelines for scientific publications.

Service Oriented Architecture (SOA) has been widely adopted by industries due to its ability to provide increased interoperability among heterogeneous systems. SOA is realized through web services which can be either based on the style of Remote Procedure call(RPC) and SOAP protocol or on the Representational State Transfer(REST) style and HTTP. RESTful web services are increasingly becoming popular in the industry because of their simplicity, uniform interface, statelessness and cacheability benefits. The ability to efficiently select and integrate appropriate heterogeneous services available on the web dynamically at runtime is essential in the development of the applications based on web services. Whenever a single web service cannot satisfy the functionality required by the user, there should be a possibility to combine several existing services together in order to fulfill the request. This need has triggered a considerable number of research efforts on the composition of web services. Although several works have focused on the composition of SOAP based web services, there has not been adequate effort towards the composition of RESTful services. However, due to the above- mentioned advantages offered by the RESTful web services, it will be beneficial to design an application as a composition of such RESTful services. Towards this objective, this paper investigates two different approaches for composing the REStful web services specifically designed for health care domain, one using the Jopera tool and another using hypermedia.

Current information extraction efforts in the biomedical domain tend to focus on finding entities and facts in structured databases or MEDLINE® abstracts. We apply a gene and protein name tagger trained on Medline abstracts (ABGene) to a randomly selected set of full text journal articles in the biomedical domain. We show the effect of adaptations made in response to the greater heterogeneity of full text.

As part of the BioNLP Open Shared Tasks 2019, the CRAFT Shared Tasks 2019 provides a platform to gauge the state of the art for three fundamental language processing tasks — dependency parse construction, coreference resolution, and ontology concept identification — over full-text biomedical articles. The structural annotation task requires the automatic generation of dependency parses for each sentence of an article given only the article text. The coreference resolution task focuses on linking coreferring base noun phrase mentions into chains using the symmetrical and transitive identity relation. The ontology concept annotation task involves the identification of concept mentions within text using the classes of ten distinct ontologies in the biomedical domain, both unmodified and augmented with extension classes. This paper provides an overview of each task, including descriptions of the data provided to participants and the evaluation metrics used, and discusses participant results relative to baseline performances for each of the three tasks.

Introduction: In recent years, the use of portfolios as learning and assessment tools has become more widespread across the range of health professions. Whilst a growing body of literature has accompanied these trends, there is no clear collated summary of the evidence for the educational effects of the use of portfolios in undergraduate education. This systematic review is the result of our work to provide such a summary.Methods: We developed a protocol based on the recommendations of the Best Evidence Medical Education (BEME) collaboration. Citations retrieved by electronic searches of 10 databases were assessed against pre-defined inclusion/exclusion criteria by two independent reviewers and full texts of potentially relevant articles were obtained. Studies were identified for inclusion in the review by examination of full text articles by two independent reviewers. At all stages, discrepancies were resolved by consensus. Data relating to characteristics of the student population, intervention, outcome...

New value-added business, generated by Web service composition in BPEL, provides more powerful and flexible features. SOAP-based web services invoked in BPEL are heavy with redundant information and lower performance. RESTful Web service is brought forward to new value-added business as an alternative solution. However, it's a big challenge to compose RESTful Web services with SOAP-based Web Services in SOA BPEL business processes. BPEL is not designed to use RESTful Web services because these types of services do not use WSDL to describe themselves. This paper proposes a novel approach for integrating RESTful Web services and SOAP-based Web services in business processes described in BPEL. The approach is applied to a practical multimedia conference BPEL process, and the experimental data show the stability of the new RESTful BPEL process with a higher performance.

Editor's Response to Letters about Rappaport et al.

PurposeStudy findings can reach a worldwide audience only after a paper is published in the peer‐reviewed literature; this is regarded by many as the definitive contribution to global exchange of knowledge. The abstract to publication (A:P) rates for free papers presented at an emergency medicine meeting in Asia has not been investigated before. The purpose of this study was to determine the full publication rate of abstracts presented as oral presentations at the Third Asian Conference on Emergency Medicine (ACEM) in Hong Kong in 2004.MethodsA detailed literature search of the MEDLINE database was performed using first and last authors' names and appropriate key words up to January 2008.ResultsA total of 54 free paper abstracts were presented at this conference as oral presentations. Ten (18.5%) abstracts had subsequently been published as full articles by the end of January 2008. The full‐text articles were published in eight different journals.ConclusionsThe A:P ratio of abstracts for oral presentations at ACEM 2004 was 18.5%, lower than that of similar meetings in the US and Australasia. It is normal for less than half of the abstracts presented at meetings to be published as full papers in refereed journals, largely due to the inability to overcome the barriers that present at each stage towards publication. Lack of researcher time due to pressure of clinical work and English language skills may play an important role in Asia.

Research and development in the area of biomedical literature analysis aims at providing life science researchers with effective means to access and exploit knowledge contained in scientific publications. Virtually all journal publications and many conference proceedings are nowadays readily available in an electronic form—for instance, as abstracts through the MEDLINE citation index or as full-text articles through PubMed Central. Nevertheless, keeping up to date with and searching for recent findings in a research domain remains a tedious task hampered by inefficient and ineffective means for access and exploitation. Biomedical text analysis aims to improve access to unstructured knowledge by alleviating searches, providing auto generated summaries of documents and topics, linking and integrating publications with structured resources, visualizing content for better understanding, and guiding researchers to novel hypotheses and into knowledge discovery. Focused research over recent years has improved fundamental solutions for biomedical text mining, such as document retrieval, named entity recognition, normalization and grounding, and extraction of relationships, with levels of accuracy that reach human annotators when considering inter annotator agreement. Consequently, more and more integrative analysis tools were put forward by the text mining community targeting a broad audience of end users: generic and task-specific search engines for life science researchers, interfaces for networks synthesis based on textual evidences, or more specialized tools searching for transcription factors, or primer sequences. This special issue of Advances in Bioinformatics presents overviews and examples of end-user-oriented biomedical text mining tools for bioinformaticians, molecular biologists, biochemists, clinicians, pharmacologists, and other researchers in life sciences. We start with A. Manconi et al. survey on “Literature retrieval and mining in bioinformatics: state of the art and challenges.” The authors introduce the major concepts that life science researchers should be familiar with getting the best out of existing text mining solutions, and survey key tools and research. In a dedicated second part of their survey, the authors address the major challenges both life science researchers and solution developers are facing at this point. The reader will find plenty of references to existing search tools, resources, and research papers. A. E. Thessen et al. focus on a particular domain, presenting an overview of “Applications of natural language processing in biodiversity science.” The authors review the application of natural processing and machine learning for biological information extraction regarding cellular processes, taxonomic names, and morphological characters. You will find detailed examples, a summary of all steps involved in information extraction, and lots of references to existing tools and resources. S. T. Ahmed et al. introduce their semantic faceted search engine, BioEve, in “A novel framework to facilitate interactive literature search.” They couple an automated extraction system with a cognitive search and navigation service, to alleviate the process of searching and browsing huge amounts of literature such as provided/delivered by MEDLINE. BioEve enables interactive query refinement and suggests concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search directions, thereby achieving semantic enrichment that improves insight gains while searching literature. S. V. Landeghem et al. present their EVEX resource in “Exploring biomolecular literature with EVEX: connecting genes through events, homology, and indirect associations.” The authors extracted more than 20 million biomolecular events involving genes and proteins, such as phosphorylation and gene regulation, from MEDLINE. The online tool generates a summary on the searched gene denoting all regulated genes, binding partners, subcellular locations, and other related data linked to the searched gene. We conclude this special issue with the paper by A. Divoli et al., discussing whether “Do Peers see more in a paper than its authors.” In a meta-analysis using automatic text analysis, they address questions such as how informative an abstract is compared to the full text; and how peers and authors might view the major contributions of a paper differently. Their analyses are comparing the information content of an abstract, as written by the paper's authors, to sentences that mention the paper as a reference, written by peers. Using this strategy, A. Divoli et al. found, for example, that citing sentences contain 20% additional concepts (likely important contributions) that were not mentioned in the abstract of the paper referred to, but maybe should have been to help attract even more peers.