Biomedical Publications Research Articles

Perspectives From Authors and Editors in the Biomedical Disciplines on Predatory Journals: Survey Study.

BackgroundPredatory journals fail to fulfill the tenets of biomedical publication: peer review, circulation, and access in perpetuity. Despite increasing attention in the lay and scientific press, no studies have directly assessed the perceptions of the authors or editors involved.ObjectiveOur objective was to understand the motivation of authors in sending their work to potentially predatory journals. Moreover, we aimed to understand the perspective of journal editors at journals cited as potentially predatory.MethodsPotential online predatory journals were randomly selected among 350 publishers and their 2204 biomedical journals. Author and editor email information was valid for 2227 total potential participants. A survey for authors and editors was created in an iterative fashion and distributed. Surveys assessed attitudes and knowledge about predatory publishing. Narrative comments were invited.ResultsA total of 249 complete survey responses were analyzed. A total of 40% of editors (17/43) surveyed were not aware that they were listed as an editor for the particular journal in question. A total of 21.8% of authors (45/206) confirmed a lack of peer review. Whereas 77% (33/43) of all surveyed editors were at least somewhat familiar with predatory journals, only 33.0% of authors (68/206) were somewhat familiar with them (P<.001). Only 26.2% of authors (54/206) were aware of Beall’s list of predatory journals versus 49% (21/43) of editors (P<.001). A total of 30.1% of authors (62/206) believed their publication was published in a predatory journal. After defining predatory publishing, 87.9% of authors (181/206) surveyed would not publish in the same journal in the future.ConclusionsAuthors publishing in suspected predatory journals are alarmingly uninformed in terms of predatory journal quality and practices. Editors’ increased familiarity with predatory publishing did little to prevent their unwitting listing as editors. Some suspected predatory journals did provide services akin to open access publication. Education, research mentorship, and a realignment of research incentives may decrease the impact of predatory publishing.

Evolution of scientific production in cardiology and cardiovascular diseases in Maghreb countries: Bibliometric study

Cardiovascular diseases represent a real public health problem in the countries of the Great Maghreb. Biomedical research focused on these diseases contributes to the planning, implementation and evaluation of different programs to combat this global burden. Thus, we conducted this bibliometric study in order to evaluate Maghrebian scientific production in the field of cardiology and cardiovascular diseases. We used the website SCImago Journal & Country Rank to collect data on Maghreb publications in the field of cardiology and cardiovascular diseases during the period 1996 to 2017. The data collected were exported to SPSS for further statistical analysis. The five Maghreb countries accounted for 14.84% of global publications in the field of cardiology and cardiovascular diseases. A significant increase in the number of published articles was observed ( r 2 = 0.799, P < 10 −3 ). It resulted in a growth index of 3.4. The number of publications increased from 14 in 1996 to 99 in 2017. The scientific research in cardiology peaked in 2015: 294 articles were identified or 23% of the total Maghreb production. Tunisia, Morocco and Algeria contributed to almost all of the biomedical cardiovascular publication with respectively 45.9%, 35.3% and 17% of publications. Thanks to the Tunisian publication in this medical discipline during the period from 1996 to 2017 (586 publications), Tunisia occupies the third position in Africa and the 59th position in the world. The number of citations per publication was 7.9. Libya had the highest number of citations per article (30.35). A significant decrease in the number of citations per document was observed in Mauritania. The H index was 27 and 19 respectively in Tunisia and Morocco. Despite its non-homogeneity, Maghrebian scientific production recorded a clear evolution from 1996 to 2017. However, the progression of cardiovascular diseases in the Maghreb countries is still alarming. It would be of high interest to study the quality of these publications as well as the areas studied.

The Deep Learning-Based Recommender System "Pubmender" for Choosing a Biomedical Publication Venue: Development and Validation Study.

BackgroundIt is of great importance for researchers to publish research results in high-quality journals. However, it is often challenging to choose the most suitable publication venue, given the exponential growth of journals and conferences. Although recommender systems have achieved success in promoting movies, music, and products, very few studies have explored recommendation of publication venues, especially for biomedical research. No recommender system exists that can specifically recommend journals in PubMed, the largest collection of biomedical literature.ObjectiveWe aimed to propose a publication recommender system, named Pubmender, to suggest suitable PubMed journals based on a paper’s abstract.MethodsIn Pubmender, pretrained word2vec was first used to construct the start-up feature space. Subsequently, a deep convolutional neural network was constructed to achieve a high-level representation of abstracts, and a fully connected softmax model was adopted to recommend the best journals.ResultsWe collected 880,165 papers from 1130 journals in PubMed Central and extracted abstracts from these papers as an empirical dataset. We compared different recommendation models such as Cavnar-Trenkle on the Microsoft Academic Search (MAS) engine, a collaborative filtering–based recommender system for the digital library of the Association for Computing Machinery (ACM) and CiteSeer. We found the accuracy of our system for the top 10 recommendations to be 87.0%, 22.9%, and 196.0% higher than that of MAS, ACM, and CiteSeer, respectively. In addition, we compared our system with Journal Finder and Journal Suggester, which are tools of Elsevier and Springer, respectively, that help authors find suitable journals in their series. The results revealed that the accuracy of our system was 329% higher than that of Journal Finder and 406% higher than that of Journal Suggester for the top 10 recommendations. Our web service is freely available at https://www.keaml.cn:8081/.ConclusionsOur deep learning–based recommender system can suggest an appropriate journal list to help biomedical scientists and clinicians choose suitable venues for their papers.

Ethical issues in biomedical publication

To determine whether demographic differences exist among editors, reviewers, and authors in The Journal of Hand Surgery (JHS). We aimed to test the null hypothesis that there would be no difference among these 3 groups with respect to gender, geographic location, academic productivity, and financial relationships with industry.Editors, reviewers, and physician authors were identified for 2018 JHS. Gender and geographic location were recorded for each person. We used the Scopus database to determine the Hirsch index (h-index) as well as the number of publications and citations for members of each group. Industry payment information was obtained using the Open Payments Web site.The editor group contained 20% women compared with the author group (17% women). Authors (59%) were less likely to be from the United States compared with editors (91%) and reviewers (88%). Editors were found to have a higher h-index (16) compared with reviewers (14) and authors (12). Authors demonstrated significantly higher mean total payments from industry ($41,738) compared with editors ($13,712) and reviewers ($20,457).In 2018, there appeared to be an even distribution with respect to gender among editors, authors and reviewers in the JHS. International editors and reviewers are relatively under-represented compared to authors. Whereas editors and reviewers demonstrated higher h-indices compared with authors, JHS authors had significantly higher mean total payments in the Open Payments database.Defining demographics, academic productivity, and conflicts of interest for journal editors, reviewers, and authors may aid in identifying potential sources of both author and peer review bias.

Rewarding the quantity of peer review could harm biomedical research.

Voluntary peer review is generally provided by researchers as a duty or service to their disciplines. They commit their expertise, knowledge and time freely without expecting rewards or compensation. Peer review can be perceived as a reciprocal mission that aims to safeguard the quality of publications by helping authors improve their manuscripts. While voluntary peer review adds value to research, rewarding the quantity or the volume of peer review is likely to lure academics into providing poor quality peer review. Consequently, the quantity of peer review may increase, but at the expense of quality, which may lead to unintended consequences and might negatively affect the quality of biomedical publications. This paper aims to present evidence that while voluntary peer review may aid researchers, pressurized peer review may create a perverse incentive that negatively affects the integrity of the biomedical research record. We closely examine one of the proposed models for rewarding peer review based on the quantity of peer review reports. This article also argues that peer review should remain a voluntary mission, and should not be prompted by the need to attain tenure or promotion.

Neural sentence embedding models for semantic similarity estimation in the biomedical domain

BackgroundNeural network based embedding models are receiving significant attention in the field of natural language processing due to their capability to effectively capture semantic information representing words, sentences or even larger text elements in low-dimensional vector space. While current state-of-the-art models for assessing the semantic similarity of textual statements from biomedical publications depend on the availability of laboriously curated ontologies, unsupervised neural embedding models only require large text corpora as input and do not need manual curation. In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature. We trained different neural embedding models on 1.7 million articles from the PubMed Open Access dataset, and evaluated them based on a biomedical benchmark set containing 100 sentence pairs annotated by human experts and a smaller contradiction subset derived from the original benchmark set.ResultsExperimental results showed that, with a Pearson correlation of 0.819, our best unsupervised model based on the Paragraph Vector Distributed Memory algorithm outperforms previous state-of-the-art results achieved on the BIOSSES biomedical benchmark set. Moreover, our proposed supervised model that combines different string-based similarity metrics with a neural embedding model surpasses previous ontology-dependent supervised state-of-the-art approaches in terms of Pearson’s r (r = 0.871) on the biomedical benchmark set. In contrast to the promising results for the original benchmark, we found our best models’ performance on the smaller contradiction subset to be poor.ConclusionsIn this study, we have highlighted the value of neural network-based models for semantic similarity estimation in the biomedical domain by showing that they can keep up with and even surpass previous state-of-the-art approaches for semantic similarity estimation that depend on the availability of laboriously curated ontologies, when evaluated on a biomedical benchmark set. Capturing contradictions and negations in biomedical sentences, however, emerged as an essential area for further work.

Figure and caption extraction from biomedical documents.

MotivationFigures and captions convey essential information in biomedical documents. As such, there is a growing interest in mining published biomedical figures and in utilizing their respective captions as a source of knowledge. Notably, an essential step underlying such mining is the extraction of figures and captions from publications. While several PDF parsing tools that extract information from such documents are publicly available, they attempt to identify images by analyzing the PDF encoding and structure and the complex graphical objects embedded within. As such, they often incorrectly identify figures and captions in scientific publications, whose structure is often non-trivial. The extraction of figures, captions and figure-caption pairs from biomedical publications is thus neither well-studied nor yet well-addressed.ResultsWe introduce a new and effective system for figure and caption extraction, PDFigCapX. Unlike existing methods, we first separate between text and graphical contents, and then utilize layout information to effectively detect and extract figures and captions. We generate files containing the figures and their associated captions and provide those as output to the end-user.We test our system both over a public dataset of computer science documents previously used by others, and over two newly collected sets of publications focusing on the biomedical domain. Our experiments and results comparing PDFigCapX to other state-of-the-art systems show a significant improvement in performance, and demonstrate the effectiveness and robustness of our approach.Availability and implementationOur system is publicly available for use at: https://www.eecis.udel.edu/~compbio/PDFigCapX. The two new datasets are available at: https://www.eecis.udel.edu/~compbio/PDFigCapX/Downloads

Context-specific interaction networks from vector representation of words

In recent years, the number of biomedical publications has steadfastly grown, resulting in a rich source of untapped new knowledge. Most biomedical facts are however not readily available, but buried in the form of unstructured text, and hence their exploitation requires the time-consuming manual curation of published articles. Here we present INtERAcT, a novel approach to extract protein-protein interactions from a corpus of biomedical articles related to a broad range of scientific domains in a completely unsupervised way. INtERAcT exploits vector representation of words, computed on a corpus of domain specific knowledge, and implements a new metric that estimates an interaction score between two molecules in the space where the corresponding words are embedded. We demonstrate the power of INtERAcT by reconstructing the molecular pathways associated to 10 different cancer types using a corpus of disease-specific articles for each cancer type. We evaluate INtERAcT using STRING database as a benchmark, and show that our metric outperforms currently adopted approaches for similarity computation at the task of identifying known molecular interactions in all studied cancer types. Furthermore, our approach does not require text annotation, manual curation or the definition of semantic rules based on expert knowledge, and hence it can be easily and efficiently applied to different scientific domains. Our findings suggest that INtERAcT may increase our capability to summarize the understanding of a specific disease using the published literature in an automated and completely unsupervised fashion.

Semi-automated fact-checking of nucleotide sequence reagents in biomedical research publications: The Seek & Blastn tool.

Nucleotide sequence reagents are verifiable experimental reagents in biomedical publications, because their sequence identities can be independently verified and compared with associated text descriptors. We have previously reported that incorrectly identified nucleotide sequence reagents are characteristic of highly similar human gene knockdown studies, some of which have been retracted from the literature on account of possible research fraud. Because of the throughput limitations of manual verification of nucleotide sequences, we developed a semi-automated fact checking tool, Seek & Blastn, to verify the targeting or non-targeting status of published nucleotide sequence reagents. From previously described and unknown corpora of 48 and 155 publications, respectively, Seek & Blastn correctly extracted 304/342 (88.9%) and 1066/1522 (70.0%) nucleotide sequences and a predicted targeting/ non-targeting status. Seek & Blastn correctly predicted the targeting/ non-targeting status of 293/304 (96.4%) and 988/1066 (92.7%) of the correctly extracted nucleotide sequences. A total of 38/39 (97.4%) or 31/79 (39.2%) Seek & Blastn predictions of incorrect nucleotide sequence reagent use were correct in the two literature corpora. Combined Seek & Blastn and manual analyses identified a list of 91 misidentified nucleotide sequence reagents, which could be built upon through future studies. In summary, incorrect nucleotide sequence reagents represent an under-recognized source of error within the biomedical literature, and fact checking tools such as Seek & Blastn may help to identify papers and manuscripts affected by these errors.

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications.

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications. We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm. Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents. CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

From POS tagging to dependency parsing for biomedical event extraction

BackgroundGiven the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of approaches to this information extraction task on syntactic information, it is valuable to understand which approaches to syntactic processing of biomedical text have the highest performance.ResultsWe perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT. To the best of our knowledge, there is no recent work making such comparisons in the biomedical context; specifically no detailed analysis of neural models on this data is available. Experimental results show that in general, the neural models outperform the feature-based models on two benchmark biomedical corpora GENIA and CRAFT. We also perform a task-oriented evaluation to investigate the influences of these models in a downstream application on biomedical event extraction, and show that better intrinsic parsing performance does not always imply better extrinsic event extraction performance.ConclusionWe have presented a detailed empirical study comparing traditional feature-based and neural network-based models for POS tagging and dependency parsing in the biomedical context, and also investigated the influence of parser selection for a biomedical event extraction downstream task.Availability of data and materialsWe make the retrained models available at https://github.com/datquocnguyen/BioPosDep.

Usage of the Term Big Data in Biomedical Publications: A Text Mining Approach

In this study, we attempt to assess the value of the term Big Data when used by researchers in their publications. For this purpose, we systematically collected a corpus of biomedical publications that use and do not use the term Big Data. These documents were used as input to a machine learning classifier to determine how well they can be separated into two groups and to determine the most distinguishing classification features. We generated 100 classifiers that could correctly distinguish between Big Data and non-Big Data documents with an area under the Receiver Operating Characteristic (ROC) curve of 0.96. The differences between the two groups were characterized by terms specific to Big Data themes—such as `computational’, `mining’, and `challenges’—and also by terms that indicate the research field, such as `genomics’. The ROC curves when plotted for various time intervals showed no difference over time. We conclude that there is a detectable and stable difference between publications that use the term Big Data and those that do not. Furthermore, the use of the term Big Data within a publication seems to indicate a distinct type of research in the biomedical field. Therefore, we conclude that value can be attributed to the term Big Data when used in a publication and this value has not changed over time.

Status of retraction notices for biomedical publications associated with research misconduct

In order to assess the status of retraction notices for publications involving research misconduct, we collected and analyzed information from the Office of Research Integrity website. This site lists confirmed instances of misconduct in research supported by the National Institutes of Health. Over a 10-year period, 200 publications derived from misconduct were identified. For 20.5% of those papers, no retraction notice was published. We found that the majority of these cases were from investigations concluded at least two years before our analysis, and thus are unlikely to be explainable by timing considerations. These findings demonstrate that retraction notices for papers associated with misconduct are often not published and suggest that clear, adherent policies are needed in this circumstance to correct the scientific record.

If Research Libraries and Funders Finance Open Access: Moving Beyond Subscriptions and APCs

Following the examples of SCOAP3, in which libraries fund open access, and eLife, in which funding agencies have begun to directly fund open access scholarly publishing, this study presents an analysis of how creatively combining these two models might provide a means to move toward universal open access (without APCs). This study calculates the publishing costs for the funders that sponsor the research and for the libraries that cover unsponsored articles for two nonprofit biomedical publishers, eLife and PLOS, and the nonprofit journal aggregator BioOne. These entities represent a mix of publishing revenue models, including funder sponsorship, article processing charges (APC), and subscription fees. Using PubMed filtering and manual-sampling strategies, as well as publicly available publisher revenue data, the study found that, in 2015, 86 percent of the articles in eLife and PLOS acknowledge funder support, as do 76 percent of the articles in the largely subscription journals of BioOne. Such findings can inform libraries and funding agencies, as well as publishers, in their consideration of a direct-payment open access model, as the study (a) demonstrates the cost breakdown for funder and library support for open access among this sample of X articles; (b) posits how publishing data-management organizations such as Crossref and ORCID can facilitate such a model of funder and library per-article open access payments; and (c) proposes ways in which such a model offers a more efficient, equitable, and scalable approach to open access across the disciplines than the prevailing APC model, which originated with biomedical publishing.

Estimation of the place of the Russian Federation in the world publication activity ranking on priority trends in biomedicine

During 2012-2018 in the Russian Federation, pursuant to Presidential Decree No. 599 of 05.07.2012, a set of state support measures was implemented aimed at increasing the specific gravity of Russian publications indexed in international databases. The national project 'Science' (NPS) designed to realize the goals set by Presidential Decree No.204 of 05.07.2018 'On the National Goals and Strategic Tasks for Development of the Russian Federation until 2024' continues and develops the theme of increasing the publication activity of the Russian Federation in the internationalized space. One of the six NPS targets is 'The place of the Russian Federation in terms of specific gravity in the total number of articles in areas defined by the priorities of scientific and technological development in publications indexed in international databases'. The purpose of this study was to evaluate the basic value of this indicator for biomedical disciplines related to the priority direction (paragraph 20c) of the Strategy for Scientific and Technological Development of the Russian Federation. We compared volumes of publication flows of the Russian Federation in 20 biomedical disciplines with a similar indicator of countries ranked fifth in the number of publications on the subject area under consideration, indexed in the Web of Science Core Collection (WoS CC). A 5-10 fold lag of the Russian Federation was recorded for the most part of the priority areas in the field of biomedicine identified by the NTS passport. The impact of public policy measures aimed at increasing the national biomedical publication stream for the period of 2012-2018 was estimated as insufficient to meet the considered NTS target.

An effective biomedical document classification scheme in support of biocuration: addressing class imbalance.

Published literature is an important source of knowledge supporting biomedical research. Given the large and increasing number of publications, automated document classification plays an important role in biomedical research. Effective biomedical document classifiers are especially needed for bio-databases, in which the information stems from many thousands of biomedical publications that curators must read in detail and annotate. In addition, biomedical document classification often amounts to identifying a small subset of relevant publications within a much larger collection of available documents. As such, addressing class imbalance is essential to a practical classifier. We present here an effective classification scheme for automatically identifying papers among a large pool of biomedical publications that contain information relevant to a specific topic, which the curators are interested in annotating. The proposed scheme is based on a meta-classification framework using cluster-based under-sampling combined with named-entity recognition and statistical feature selection strategies. We examined the performance of our method over a large imbalanced data set that was originally manually curated by the Jackson Laboratory’s Gene Expression Database (GXD). The set consists of more than 90 000 PubMed abstracts, of which about 13 000 documents are labeled as relevant to GXD while the others are not relevant. Our results, 0.72 precision, 0.80 recall and 0.75 f-measure, demonstrate that our proposed classification scheme effectively categorizes such a large data set in the face of data imbalance.

Issues on Quality Research to Publish

Aims: To sensitize authors and new researchers to produce a quality research manuscript.&#x0D; Methods: Review of experiences and challenges faced by scientifi c journal editors and new authors; and scientifi c writing guidelines and workshops.&#x0D; Results: Impatient submission of manuscript with inadequate understanding of authors’ guidelines. Strength of own research and scope of journal not matching; lack of minimum training on writing and submission; journal publication from professional organizations not in primary priority list because of its voluntary nature of work.&#x0D; Conclusions: Publishing depends on professional commitment, academic interest and need, personal career and institutional interest, and availability of specifi c biomedical publishers.&#x0D; Keywords: authors’ guideline, biomedical journal, publication

BIOMEDICAL PUBLICATIONS PRODUCTIVITY OF ORAL AND DENTAL MEDICINE FROM LIBYA (PUBMED 2003-2013)

The purpose of this short communication study was to investigation the trends of biomedical publications productivity of oral and dentistry field from Libya using the most popular biomedical search engine PubMed with focus on Libyan academic institutions. This search was performed on articles that were related to dentistry and oral medicine using the affiliation of Libya for the period 2003-2013. A total of 358 biomedical published articles were retrieved of which only 40 were related to oral and dentistry medicine. In conclusion, this investigation shows a very low productivity and weak distribution of biomedical research in oral and dentistry medicine productivity throughout Libya.

50 years of Iranian clinical, biomedical, and public health research: a bibliometric analysis of the Web of Science Core Collection (1965-2014).

BackgroundA substantial growth has been reported in Iran’s number of clinical, biomedical, and public health research publications over the last 30 years. It is of interest to investigate whether this quantitative growth has also led to a larger number of papers with a high citation impact; to explore where the capacity for performing research lies; and which fields/institutions are lagging behind.MethodsThis was a bibliometric study. Web of Science Core Collection and its different tools were used for retrieving and analysing the publications. Information about the journals was found on Journal Citation Reports®. Different types of collaborations across the highly-cited papers was investigated based on the affiliations, the characteristics of the language of the authors’ names, and the authors’ study and work backgrounds.ResultsIran’s number of clinical, biomedical, and public health research publications has substantially increased since 2000, a surge was seen in 2007, and the figure reached a peak in 2011. 11% of the publications were in Pharmacology Pharmacy; and the majority originated in Tehran University of Medical Sciences. Six of the 10 journals that had published the most were Iranian journals. H-index of publications had also increased over time (almost doubled between 2000 and 2010). 30.9% of the most-cited publications had only relied on Iranian resources (including 48 publications); had been published in journals with impact factors ranging between 0.4 and 8.3; and the majority were original basic sciences research.ConclusionsIn Iran, a great capacity for research lies in clinical, biomedical, and public health fields which can be strengthened with further investment. It is important to use this capacity in a way that would align with the national population health needs. It is also essential to consider the limitations of only relying on bibliometric tools for assessing health research activities. Finally, the Iranian science policy-makers are encouraged to (i) support the researchers and institutions that have proved research capacity; (ii) direct further resources towards research areas and/or institutions that are lagging behind; (iii) facilitate further international collaboration with the academics and/or institutions that have shown the capacity for conducting successful research projects with Iran.