The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/

Background: Biomedical named entity recognition (Bio-NER) is a challenging problem because, in general, biomedical named entities of the same category (e.g., proteins and genes) do not follow one standard nomenclature. They have many irregularities and sometimes appear in ambiguous contexts. In recent years, machine-learning (ML) approaches have become increasingly common and now represent the cutting edge of Bio-NER technology. This paper addresses three problems faced by ML-based Bio-NER systems. First, most ML approaches usually employ singleton features that comprise one linguistic property (e.g., the current word is capitalized) and at least one class tag (e.g., B-protein, the beginning of a protein name). However, such features may be insufficient in cases where multiple properties must be considered. Adding conjunction features that contain multiple properties can be beneficial, but it would be infeasible to include all conjunction features in an NER model since memory resources are limited and some features are ineffective. To resolve the problem, we use a sequential forward search algorithm to select an effective set of features. Second, variations in the numerical parts of biomedical terms (e.g., "2" in the biomedical term IL2) cause data sparseness and generate many redundant features. In this case, we apply numerical normalization, which solves the problem by replacing all numerals in a term with one representative numeral to help classify named entities. Third, the assignment of NE tags does not depend solely on the target word's closest neighbors, but may depend on words outside the context window (e.g., a context window of five consists of the current word plus two preceding and two subsequent words). We use global patterns generated by the Smith-Waterman local alignment algorithm to identify such structures and modify the results of our ML-based tagger. This is called pattern-based post-processing.

Gene Ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation.Database URL: http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/.

Gastric cancer is one of the most aggressive cancers and is the second leading cause of cancer death worldwide. Approximately 40% of global gastric cancer cases occur in China, with peritoneal metastasis being the prevalent form of recurrence and metastasis in advanced disease. Currently, there are limited clinical approaches for predicting and treatment of peritoneal metastasis, resulting in a 6-month average survival time. By comprehensive genome analysis will uncover the pathogenesis of peritoneal metastasis. Here we describe a comprehensive whole-genome and transcriptome sequencing analysis of one advanced gastric cancer case, including non-cancerous mucosa, primary cancer and matched peritoneal metastatic cancer. The peripheral blood is used as normal control. We identified 27 mutated genes, of which 19 genes are reported in COSMIC database (ZNF208, CRNN, ATXN3, DCTN1, RP1L1, PRB4, PRB1, MUC4, HS6ST3, MUC17, JAM2, ITGAD, IREB2, IQUB, CORO1B, CCDC121, AKAP2, ACAN and ACADL), and eight genes have not previously been described in gastric cancer (CCDC178, ARMC4, TUBB6, PLIN4, PKLR, PDZD2, DMBT1and DAB1).Additionally,GPX4 and MPND in 19q13.3-13.4 region, is characterized as a novel fusion-gene. This study disclosed novel biological markers and tumorigenic pathways that would predict gastric cancer occurring peritoneal metastasis.

The IL-23/IL-17 pathway is important in multiple autoimmune diseases, but its effect on lupus pathology remains unclear with opposing trials in murine models of the disease. Herein we show a disease activity related upregulation of serum IL-23 and IL-23 receptor in patients with SLE as compared to healthy controls. IL-23 when added in SLE T cell in vitro cultures, induced IL-17 and limited IL-2 production while Tfh and DN T cells significantly expanded. To further dissect the role of IL-23 in the expression of autoimmunity and related pathology, we generated IL-23 receptor deficient MRL.lpr mice. These IL-23R−/−MRL.lpr mice displayed attenuated lupus nephritis with a striking decrease in the accumulation of double negative T (DN T) cells in the kidneys and secondary lymphoid organs. Moreover, T cells from IL-23R−/−MRL.lpr mice produced increased amounts of IL-2 and reduced amounts of IL-17 compared to T cells from wild type animals. In vitro IL-23 treatment promoted IL-17 production and downregulated IL-2 production. The IL-23R−/−MRL.lpr had fewer T follicular helper cells (Tfh), B cells and plasma cells, leading to decreased production of anti-dsDNA antibodies. Our results show that IL-23 accounts for main aspects of human and murine lupus including the expansion of DN T cells, decreased IL-2 and increased IL-17 production. We propose that blockade of IL23 should have a therapeutic value in patients with SLE.