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

Sonic hedgehog multimerization: A self-organizing event driven by post-translational modifications?

<p>Sonic hedgehog (Shh) is a morphogen active during vertebrate development and tissue homeostasis in adulthood. Dysregulation of the Shh signalling pathway is known to incite carcinogenesis. Due to the highly lipophilic nature of this protein imparted by two post-translational modifications, Shh’s method of transit through the aqueous extracellular milieu has been a long-standing conundrum, prompting the proposition of numerous hypotheses to explain the manner of its displacement from the surface of the producing cell. Detection of high molecular-weight complexes of Shh in the intercellular environment has indicated that the protein achieves this by accumulating into multimeric structures prior to release from producing cells. The mechanism of assembly of the multimers, however, has hitherto remained mysterious and contentious. Here, with the aid of high-resolution optical imaging and post-translational modification mutants of Shh, we show that the C-terminal cholesterol and the N-terminal palmitate adducts contribute to the assembly of large multimers and regulate their shape. Moreover, we show that small Shh multimers are produced in the absence of any lipid modifications. Based on an assessment of the distribution of various dimensional characteristics of individual Shh clusters, in parallel with deductions about the kinetics of release of the protein from the producing cells, we conclude that multimerization is driven by self-assembly underpinned by the law of mass action. We speculate that the lipid modifications augment the size of the multimolecular complexes through prolonging their association with the exoplasmic membrane.</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