SocioPedia+: a visual analytics system for social knowledge graph-based event exploration.

In the recent era of information explosion, exploring event from social networks has recently been a crucial task for many applications. To derive valuable comprehensive and thorough insights on social events, visual analytics (VA) system have been broadly used as a promising solution. However, due to the enormous social data volume with highly diversity and complexity, the number of event exploration tasks which can be enabled in a conventional real-time visual analytics systems has been limited.

Automated Classification of Normal Control and Early-Stage Dementia Based on Activities of Daily Living (ADL) Data Acquired from Smart Home Environment.

With the global trend toward an aging population, the increasing number of dementia patients and elderly living alone has emerged as a serious social issue in South Korea. The assessment of activities of daily living (ADL) is essential for diagnosing dementia. However, since the assessment is based on the ADL questionnaire, it relies on subjective judgment and lacks objectivity.

Medical Health Records-Based Mild Cognitive Impairment (MCI) Prediction for Effective Dementia Care.

Dementia is a cognitive impairment that poses a global threat. Current dementia treatments slow the progression of the disease. The timing of starting such treatment markedly affects the effectiveness of the treatment.

Gender-Based Analysis of Risk Factors for Dementia Using Senior Cohort.

Globally, one of the biggest problems with the increase in the elderly population is dementia. However, dementia still has no fundamental cure. Therefore, it is important to predict and prevent dementia early.

Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease.

Interest in research involving health-medical information analysis based on artificial intelligence, especially for deep learning techniques, has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. These features are extracted by analyzing various clinical pathology data, such as EHR (electronic health records), and academic literature using the techniques of data analysis, natural language processing, etc.

Longitudinal Study-Based Dementia Prediction for Public Health.

The issue of public health in Korea has attracted significant attention given the aging of the country's population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea.

Overview of the Cancer Genetics and Pathway Curation tasks of BioNLP Shared Task 2013.

Background: Since their introduction in 2009, the BioNLP Shared Task events have been instrumental in advancing the development of methods and resources for the automatic extraction of information from the biomedical literature. In this paper, we present the Cancer Genetics (CG) and Pathway Curation (PC) tasks, two event extraction tasks introduced in the BioNLP Shared Task 2013. The CG task focuses on cancer, emphasizing the extraction of physiological and pathological processes at various levels of biological organization, and the PC task targets reactions relevant to the development of biomolecular pathway models, defining its extraction targets on the basis of established pathway representations and ontologies.

A Case of the Drug Reaction with Eosinophilia and Systemic Symptom (DRESS) Following Isoniazid Treatment.

The drug reaction with eosinophilia and systemic symptom (DRESS) syndrome is a severe adverse drug-induced reaction which includes a severe skin eruption, fever, hematologic abnormalities (eosinophilia or atypical lymphocytes) and internal organ involvement. The most frequently reported drug was anticonvulsants. The diagnosis of DRESS syndrome is challenging because the pattern of cutaneous eruption and the types of organs involved are various.

Risk of malignancy in thyroid incidentalomas identified by fluorodeoxyglucose-positron emission tomography.

Background: Thyroid incidentalomas detected by 2-deoxy-2-18F-fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) have been reported in 1% to 4% of the population, with a risk of malignancy of 27.8% to 74%. We performed a retrospective review of FDG-avid thyroid incidentalomas in cancer screening subjects and patients with nonthyroid cancer.

The 3rd DBCLS BioHackathon: improving life science data integration with Semantic Web technologies.

Background: BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research.

Results: The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces.

U-Compare bio-event meta-service: compatible BioNLP event extraction services.

Background: Bio-molecular event extraction from literature is recognized as an important task of bio text mining and, as such, many relevant systems have been developed and made available during the last decade. While such systems provide useful services individually, there is a need for a meta-service to enable comparison and ensemble of such services, offering optimal solutions for various purposes.

Results: We have integrated nine event extraction systems in the U-Compare framework, making them intercompatible and interoperable with other U-Compare components.

The 2nd DBCLS BioHackathon: interoperable bioinformatics Web services for integrated applications.

Background: The interaction between biological researchers and the bioinformatics tools they use is still hampered by incomplete interoperability between such tools. To ensure interoperability initiatives are effectively deployed, end-user applications need to be aware of, and support, best practices and standards. Here, we report on an initiative in which software developers and genome biologists came together to explore and raise awareness of these issues: BioHackathon 2009.

The DBCLS BioHackathon: standardization and interoperability for bioinformatics web services and workflows. The DBCLS BioHackathon Consortium*.

Web services have become a key technology for bioinformatics, since life science databases are globally decentralized and the exponential increase in the amount of available data demands for efficient systems without the need to transfer entire databases for every step of an analysis. However, various incompatibilities among database resources and analysis services make it difficult to connect and integrate these into interoperable workflows. To resolve this situation, we invited domain specialists from web service providers, client software developers, Open Bio* projects, the BioMoby project and researchers of emerging areas where a standard exchange data format is not well established, for an intensive collaboration entitled the BioHackathon 2008.

The H-Invitational Database (H-InvDB), a comprehensive annotation resource for human genes and transcripts.

Here we report the new features and improvements in our latest release of the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/), a comprehensive annotation resource for human genes and transcripts.

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts.

Background: Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations.

Extraction of gene-disease relations from Medline using domain dictionaries and machine learning.

We describe a system that extracts disease-gene relations from Medline. We constructed a dictionary for disease and gene names from six public databases and extracted relation candidates by dictionary matching. Since dictionary matching produces a large number of false positives, we developed a method of machine learning-based named entity recognition (NER) to filter out false recognitions of disease/gene names.