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

Choline transporter mutations in severe congenital myasthenic syndrome disrupt transporter localization.

The presynaptic, high-affinity choline transporter is a critical determinant of signalling by the neurotransmitter acetylcholine at both central and peripheral cholinergic synapses, including the neuromuscular junction. Here we describe an autosomal recessive presynaptic congenital myasthenic syndrome presenting with a broad clinical phenotype due to homozygous choline transporter missense mutations. The clinical phenotype ranges from the classical presentation of a congenital myasthenic syndrome in one patient (p.Pro210Leu), to severe neurodevelopmental delay with brain atrophy (p.Ser94Arg) and extend the clinical outcomes to a more severe spectrum with infantile lethality (p.Val112Glu). Cells transfected with mutant transporter construct revealed a virtually complete loss of transport activity that was paralleled by a reduction in transporter cell surface expression. Consistent with these findings, studies to determine the impact of gene mutations on the trafficking of the Caenorhabditis elegans choline transporter orthologue revealed deficits in transporter export to axons and nerve terminals. These findings contrast with our previous findings in autosomal dominant distal hereditary motor neuropathy of a dominant-negative frameshift mutation at the C-terminus of choline transporter that was associated with significantly reduced, but not completely abrogated choline transporter function. Together our findings define divergent neuropathological outcomes arising from different classes of choline transporter mutation with distinct disease processes and modes of inheritance. These findings underscore the essential role played by the choline transporter in sustaining acetylcholine neurotransmission at both central and neuromuscular synapses, with important implications for treatment and drug selection

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