Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Periodontitis, porphyromonas, and the pathogenesis of rheumatoid arthritis

Epidemiological data indicate a link between rheumatoid arthritis (RA) and periodontal disease (PD). In vitro and in vivo studies have sought to dissect potential mechanisms by which PD may contribute to initiation and progression of RA. However, these are both multifactorial, chronic diseases, and their complex etiologies and pathogenesis themselves remain incompletely understood. Could there really be an etiological link or does this simply represent a statistical coincidence muddied by common risk factors? This review seeks to provide background on these two diseases in the context of recent discoveries suggesting that their pathogenesis may be related. In particular, the process of citrullination, a post-translational protein modification, has been highlighted as a process common to both diseases. The evidence for a relationship between the diseases is explored and its potential mechanisms discussed

The ecology of freshwater planarians.

Planarians are on the rise as a model system for regeneration and stem cell dynamics. Almost in parallel the interest in planarian field biology has declined. Besides representing an independent research discipline in its own right, understanding of the natural habitat is also directly relevant to optimizing culture conditions in the laboratory. Moreover, the current laboratory models are but few of hundreds of planarian species worldwide. Their adaptation to a wide range of ecological niches has resulted in a fascinating diversity of regenerative abilities, body size, reproduction strategies, and life expectancy, to name just a few. With the currently ongoing establishment of large planarian species collections, such phenotypic diversity becomes accessible to comparative mechanistic analysis in the laboratory. Overall, we hope that this chapter inspires an integral view of the planarian model system that not only includes the molecular and cellular processes under investigation but also the evolutionary forces that shaped them in the first place

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press