The overview of the BioRED (Biomedical Relation Extraction Dataset) track at BioCreative VIII.

The BioRED track at BioCreative VIII calls for a community effort to identify, semantically categorize, and highlight the novelty factor of the relationships between biomedical entities in unstructured text. Relation extraction is crucial for many biomedical natural language processing (NLP) applications, from drug discovery to custom medical solutions. The BioRED track simulates a real-world application of biomedical relationship extraction, and as such, considers multiple biomedical entity types, normalized to their specific corresponding database identifiers, as well as defines relationships between them in the documents.

Methicillin-resistant Staphylococcus aureus nasal colonization among HIV-infected patients in Taiwan: prevalence, molecular characteristics and associated factors with nasal carriage.

Background: To evaluate nasal carriage, antibiotic susceptibility and molecular characteristics of methicillin-resistant Staphylococcus aureus (MRSA), as well as the risk factors of MRSA colonization, in human immunodeficiency virus (HIV)-infected patients in northern Taiwan.

Methods: From September 2014 to November 2015, HIV-infected patients seeking outpatient care at four hospitals were eligible for this study. A nasal specimen was obtained from each subject for the detection of S.

Using deep learning to identify translational research in genomic medicine beyond bench to bedside.

Tracking scientific research publications on the evaluation, utility and implementation of genomic applications is critical for the translation of basic research to impact clinical and population health. In this work, we utilize state-of-the-art machine learning approaches to identify translational research in genomics beyond bench to bedside from the biomedical literature. We apply the convolutional neural networks (CNNs) and support vector machines (SVMs) to the bench/bedside article classification on the weekly manual annotation data of the Public Health Genomics Knowledge Base database.

Overview of the BioCreative VI text-mining services for Kinome Curation Track.

The text-mining services for kinome curation track, part of BioCreative VI, proposed a competition to assess the effectiveness of text mining to perform literature triage. The track has exploited an unpublished curated data set from the neXtProt database. This data set contained comprehensive annotations for 300 human protein kinases.

AuDis: an automatic CRF-enhanced disease normalization in biomedical text.

Diseases play central roles in many areas of biomedical research and healthcare. Consequently, aggregating the disease knowledge and treatment research reports becomes an extremely critical issue, especially in rapid-growth knowledge bases (e.g.

Curatable Named-Entity Recognition Using Semantic Relations.

Named-entity recognition (NER) plays an important role in the development of biomedical databases. However, the existing NER tools produce multifarious named-entities which may result in both curatable and non-curatable markers. To facilitate biocuration with a straightforward approach, classifying curatable named-entities is helpful with regard to accelerating the biocuration workflow.

Using a search engine-based mutually reinforcing approach to assess the semantic relatedness of biomedical terms.

Background: Determining the semantic relatedness of two biomedical terms is an important task for many text-mining applications in the biomedical field. Previous studies, such as those using ontology-based and corpus-based approaches, measured semantic relatedness by using information from the structure of biomedical literature, but these methods are limited by the small size of training resources. To increase the size of training datasets, the outputs of search engines have been used extensively to analyze the lexical patterns of biomedical terms.

CoIN: a network analysis for document triage.

In recent years, there was a rapid increase in the number of medical articles. The number of articles in PubMed has increased exponentially. Thus, the workload for biocurators has also increased exponentially.