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

Perceived Support from Best Friends and Depressive Symptoms During Adolescence: Disentangling Personal from Dyadic Level Effects

Support from best friends is an important interpersonal factor in adolescent depression development but is often studied from an individual perspective in which dyadic effects are overlooked. This study aims to a) test whether differences in support vary at the individual level and are related to individual differences in the development of depressive symptoms, whether these differences vary at the dyadic level and are related to dyadic depression symptom development, or both, b) explore whether these associations are moderated by initial levels of depressive symptoms on the individual and/or dyadic level. Data from 452 adolescents (Mage = 13.03), nested in 226 same-gender friendship dyads (60.6% boy-dyads) who participated in the RADAR-Y project were included. Best friends self-reported annually (2006–2008; 3 waves) on their own depressive symptoms and perceived support from their friend. Multilevel models showed no direct association between support and depression development on the individual or dyadic level. However, the initial level of dyads’ depressive symptoms moderated the association between dyadic support and dyads’ subsequent depression symptom development. When dyads experienced relatively more initial depressive symptoms, higher levels of dyadic support were associated with relative increasing dyadic depressive symptoms. When dyads experienced relatively few initial depressive symptoms, higher levels of dyadic support were associated with relative decreasing dyadic depressive symptoms. Findings suggest that support from best friends can either protect against or exacerbate the development of depressive symptoms for friends, depending on the initial level of depressive symptoms of the dyad

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