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

Intraguild Prey Served as Alternative Prey for Intraguild Predators in a Reciprocal Predator Guild between <i>Neoseiulus barkeri</i> and <i>Scolothrips takahashii</i>

The predatory mites Neoseiulus barkeri (Hughes) and the predatory thrips Scolothrips takahashii (Priesner) are known as potential biocontrol agents for the two-spotted spider mite Tetranychus urticae (Koch). These two predator species occur simultaneously on crops in agricultural ecosystems and are proved to be involved in life-stage specific intraguild predation. The intraguild prey may play a role in securing the persistence of the intraguild predators during food shortage periods. To understand the potential of intraguild prey as food source for intraguild predators in the N. barkeri and S. takahashii guild at low T. urticae densities, the survival, development and reproduction of both predators was determined when fed on heterospecific predators. The choice tests were conducted to determine the preference of the intraguild predator between the intraguild prey and the shared prey. Results showed that 53.3% N. barkeri and 60% S. takahashii juveniles successfully developed when fed on heterospecific predators. Female intraguild predators of both species fed on intraguild prey survived and laid eggs throughout the experiment. In the choice test, both intraguild predator species preferred their extraguild prey T. urticae. This study suggested that intraguild prey served as an alternative prey for intraguild predators prolonged survival and ensured the reproduction of intraguild predators during food shortage, ultimately decreasing the need for the continual release of the predators

Association analysis between <i>HIF2A</i> tSNPs and levels of high altitude among native Tibetans.

<p>Abbreviations: Additive, additive model; Dominant, dominant model.</p><p><i>P</i>-values except the noted ones are calculated from χ² test.</p><p>^<b>a</b><i>P</i>-values are calculated from Fisher exact test.</p><p>^<b>b</b> Bold type denotes <i>P</i><0.05.</p><p>Association analysis between <i>HIF2A</i> tSNPs and levels of high altitude among native Tibetans.</p

Clinical characteristics of the studied groups.

<p>Abbreviations: BMI, body mass index; RBC, red blood cell count, RBC; HB, hemoglobin; HCT, hematocrit; LVEF, left ventricular ejection fraction.</p><p>^<b>a</b> Data are means ± SD.</p><p>Clinical characteristics of the studied groups.</p

Correlation of allele frequencies of <i>HIF2A</i> variants in Tibetans and level of high altitude.

<p>solid circle, rs56721780-C; solid diamond, rs6756667-A; solid square, rs7589621-G; solid triangle, rs1868092-A</p

Association analysis between <i>HIF1A</i> tSNPs and levels of high altitude among native Tibetans.

<p>Abbreviations: Additive, additive model; Dominant, dominant model.</p><p><i>P</i>-values except the noted ones are calculated from χ² test.</p><p>^<b>a</b><i>P</i>-values are calculated from Fisher exact test.</p><p>^<b>b</b> Bold type denotes <i>P</i><0.05.</p><p>Association analysis between <i>HIF1A</i> tSNPs and levels of high altitude among native Tibetans.</p

Allele Frequencies of the altitude-associated <i>HIF2A</i> tSNPs between populations.

<p>Abbreviations: TBT, Tibetan in Tibet; TBQ, Tibetan in Qinghai; CHB, Chinese Han in Beijing. Japanese in Tokyo, Japan.</p><p>^<b>a</b> From 1000 GENOMES, phase 1.</p><p>^<b>b</b> From Xu <i>et al</i>.2011.</p><p>^<b>c</b> From Simonson <i>et al</i>.2010.</p><p>^<b>d</b> No data.</p><p>Allele Frequencies of the altitude-associated <i>HIF2A</i> tSNPs between populations.</p

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