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

Genomic organization of the CC chemokine MIP-3 alpha/CCL20/LARC/EXODUS/SCYA20, showing gene structure, splice variants, and chromosome localization

Copyright © 2001 Academic Press. All rights reserved.We describe the genomic organization of a recently identified CC chemokine, MIP3α/CCL20 (HGMW-approved symbol SCYA20). The MIP-3α/CCL20 gene was cloned and sequenced, revealing a four exon, three intron structure, and was localized by FISH analysis to 2q35–q36. Two distinct cDNAs were identified, encoding two forms of MIP-3α/CCL20, Ala MIP-3α/CCL20 and Ser MIP-3α/CCL20, that differ by one amino acid at the predicted signal peptide cleavage site. Examination of the sequence around the boundary of intron 1 and exon 2 showed that use of alternative splice acceptor sites could give rise to Ala MIP-3α/CCL20 or Ser MIP-3α/CCL20. Both forms of MIP-3α/CCL20 were chemically synthesized and tested for biological activity. Both flu antigen plus IL-2-activated CD4+ and CD8+ T lymphoblasts and cord blood-derived dendritic cells responded to Ser and Ala MIP-3α/CCL20. T lymphocytes exposed only to IL-2 responded inconsistently, while no response was detected in naive T lymphocytes, monocytes, or neutrophils. The biological activity of Ser MIP-3α/CCL20 and Ala MIP-3α/CCL20 and the tissue-specific preference of different splice acceptor sites are not yet known.Robin T. Nelson, James Boyd, Ronald P. Gladue, Timothy Paradis, Ranjeny Thomas, Ann C. Cunningham, Paul Lira, William H. Brissette, Lisa Hayes, Lynn M. Hames, Kuldeep S. Neote and Shaun R. McCollhttp://www.elsevier.com/wps/find/journaldescription.cws_home/622838/description#descriptio

Expression of rat I-TAC/CXCL11/SCYA11 during central nervous system inflammation: comparison with other CXCR3 ligands

© 2007 United States and Canadian Academy of PathologyThe chemokines are a large gene superfamily with critical roles in development and immunity. The chemokine receptor CXCR3 appears to play a major role in the trafficking of activated Th1 lymphocytes. There are at least three major ligands for CXCR3: mig/CXCL9, IP-10/CXCL10 and I-TAC/CXCL11, and of these three ligands, CXCL11 is the least well-characterized. In this study, we have cloned a rat ortholog of CXCL11, evaluated its function, and examined its expression in the Th-1-mediated disease, experimental autoimmune encephalomyelitis (EAE) in the rat. Based on its predicted primary amino-acid sequence, rat I-TAC/CXCL11 was synthesized and shown to induce chemotaxis of activated rat T lymphocytes in vitro and the in vivo migration of T lymphocytes when injected into the skin. I-TAC/CXCL11 expression, as determined by RT-PCR, increased in lymph node and spinal cord tissue collected from rats in which EAE had been actively induced, and in spinal cord tissue from rats in which EAE had been passively induced. The kinetics of expression were similar to that of CXCR3 and IP-10/CXCL10, although expression of both CXCR3 and IP-10/CXCL10 was more intense than that of I-TAC/CXCL11 and increased more rapidly in both lymph nodes and the spinal cord. Only minor levels of expression of the related chemokine mig/CXCL9 were observed. Immunohistochemistry revealed that the major cellular source of I-TAC/CXCL11 in the central nervous system (CNS) during EAE is likely to be the astrocyte. Together, these data indicate that I-TAC/CXCL11 is expressed in the CNS during the clinical phase of EAE. However, the observation that I-TAC/CXCL11 is expressed after receptor expression is detected suggests that it is not essential for the initial migration of CXCR3-bearing cells into the CNS.Shaun R McColl, Surendran Mahalingam, Maria Staykova, Laurie A Tylaska, Katherine E Fisher, Christine A Strick, Ronald P Gladue, Kuldeep S Neote and David O Willenbor

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