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

Comparative genomics-informed design of two LAMP detection assays for detection of the kiwifruit pathogen Pseudomonas syringae pv. actinidiae and discrimination of isolates belonging to the pandemic biovar 3

The aim of this study was to develop a rapid, sensitive and reliable field-based assay for detection of the quarantine pathogen Pseudomonas syringae pv. actinidiae, the causal agent of the most destructive and economically important bacterial disease of kiwifruit. A comparative genomic approach was used on the publicly available P. syringae pv. actinidiae genomic data to select unique target regions for the development of two loop-mediated isothermal amplification (LAMP) assays able to detect P. syringae pv. actinidiae and to discriminate strains belonging to the highly virulent and globally spreading P. syringae pv. actinidiae biovar 3. Both LAMP assays showed specificity in accordance to their target and were able to detect reliably 125 CFU per reaction in less than 30 min. The developed assays were able to detect the presence of P. syringae pv. actinidiae in symptomatic as well as in asymptomatic naturally infected kiwifruit material, thus increasing the potential of the LAMP assays for phytosanitary purposes