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

Common variants in KCNN3 are associated with lone atrial fibrillation

Atrial fibrillation (AF) is the most common sustained arrhythmia. Previous studies have identified several genetic loci associated with typical AF. We sought to identify common genetic variants underlying lone AF. This condition affects a subset of individuals without overt heart disease and with an increased heritability of AF. We report a meta-analysis of genome-wide association studies conducted using 1,335 individuals with lone AF (cases) and 12,844 unaffected individuals (referents). Cases were obtained from the German AF Network, Heart and Vascular Health Study, the Atherosclerosis Risk in Communities Study, the Cleveland Clinic and Massachusetts General Hospital. We identified an association on chromosome 1q21 to lone AF (rs13376333, adjusted odds ratio = 1.56; P = 6.3 × 10 12), and we replicated this association in two independent cohorts with lone AF (overall combined odds ratio = 1.52, 95% CI 1.40-1.64; P = 1.83 × 10 21). rs13376333 is intronic to KCNN3, which encodes a potassium channel protein involved in atrial repolarization