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

Pathogenic VCP/TER94 Alleles Are Dominant Actives and Contribute to Neurodegeneration by Altering Cellular ATP Level in a Drosophila IBMPFD Model

Inclusion body myopathy with Paget's disease of bone and frontotemporal dementia (IBMPFD) is caused by mutations in Valosin-containing protein (VCP), a hexameric AAA ATPase that participates in a variety of cellular processes such as protein degradation, organelle biogenesis, and cell-cycle regulation. To understand how VCP mutations cause IBMPFD, we have established a Drosophila model by overexpressing TER94 (the sole Drosophila VCP ortholog) carrying mutations analogous to those implicated in IBMPFD. Expression of these TER94 mutants in muscle and nervous systems causes tissue degeneration, recapitulating the pathogenic phenotypes in IBMPFD patients. TER94-induced neurodegenerative defects are enhanced by elevated expression of wild-type TER94, suggesting that the pathogenic alleles are dominant active mutations. This conclusion is further supported by the observation that TER94-induced neurodegenerative defects require the formation of hexamer complex, a prerequisite for a functional AAA ATPase. Surprisingly, while disruptions of the ubiquitin-proteasome system (UPS) and the ER–associated degradation (ERAD) have been implicated as causes for VCP–induced tissue degeneration, these processes are not significantly affected in our fly model. Instead, the neurodegenerative defect of TER94 mutants seems sensitive to the level of cellular ATP. We show that increasing cellular ATP by independent mechanisms could suppress the phenotypes of TER94 mutants. Conversely, decreasing cellular ATP would enhance the TER94 mutant phenotypes. Taken together, our analyses have defined the nature of IBMPFD–causing VCP mutations and made an unexpected link between cellular ATP level and IBMPFD pathogenesis