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

Persistent neuronal Ube3a expression in the suprachiasmatic nucleus of Angelman syndrome model mice

Mutations or deletions of the maternal allele of the UBE3A gene cause Angelman syndrome (AS), a severe neurodevelopmental disorder. The paternal UBE3A/Ube3a allele becomes epigenetically silenced in most neurons during postnatal development in humans and mice; hence, loss of the maternal allele largely eliminates neuronal expression of UBE3A protein. However, recent studies suggest that paternal Ube3a may escape silencing in certain neuron populations, allowing for persistent expression of paternal UBE3A protein. Here we extend evidence in AS model mice (Ube3a(m–/p+)) of paternal UBE3A expression within the suprachiasmatic nucleus (SCN), the master circadian pacemaker. Paternal UBE3A-positive cells in the SCN show partial colocalization with the neuropeptide arginine vasopressin (AVP) and clock proteins (PER2 and BMAL1), supporting that paternal UBE3A expression in the SCN is often of neuronal origin. Paternal UBE3A also partially colocalizes with a marker of neural progenitors, SOX2, implying that relaxed or incomplete imprinting of paternal Ube3a reflects an overall immature molecular phenotype. Our findings highlight the complexity of Ube3a imprinting in the brain and illuminate a subpopulation of SCN neurons as a focal point for future studies aimed at understanding the mechanisms of Ube3a imprinting