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

Major viral diseases in grain legumes: designing disease resistant legumes from plant breeding and OMICS integration

Grain legumes play a crucial role in human nutrition and as a staple crop for low-income farmers in developing and underdeveloped nations, contributing to overall food security and agroecosystem services. Viral diseases are major biotic stresses that severely challenge global grain legume production. In this review, we discuss how exploring naturally resistant grain legume genotypes within germplasm, landraces, and crop wild relatives could be used as promising, economically viable, and eco-environmentally friendly solution to reduce yield losses. Studies based on Mendelian and classical genetics have enhanced our understanding of key genetic determinants that govern resistance to various viral diseases in grain legumes. Recent advances in molecular marker technology and genomic resources have enabled us to identify genomic regions controlling viral disease resistance in various grain legumes using techniques such as QTL mapping, genome-wide association studies, whole-genome resequencing, pangenome and ‘omics’ approaches. These comprehensive genomic resources have expedited the adoption of genomics-assisted breeding for developing virus-resistant grain legumes. Concurrently, progress in functional genomics, especially transcriptomics, has helped unravel underlying candidate gene(s) and their roles in viral disease resistance in legumes. This review also examines the progress in genetic engineering-based strategies, including RNA interference, and the potential of synthetic biology techniques, such as synthetic promoters and synthetic transcription factors, for creating viral-resistant grain legumes. It also elaborates on the prospects and limitations of cutting-edge breeding technologies and emerging biotechnological tools (e.g., genomic selection, rapid generation advances, and CRISPR/Cas9-based genome editing tool) in developing virus-disease-resistant grain legumes to ensure global food security