An Integrated Children Disease Prediction Tool within a Special Social Network.

Stud Health Technol Inform. 2016;221:69-73

Authors: Apostolova Trpkovska M, Yildirim Yayilgan S, Besimi A

Abstract
This paper proposes a social network with an integrated children disease prediction system developed by the use of the specially designed Children General Disease Ontology (CGDO). This ontology consists of children diseases and their relationship with symptoms and Semantic Web Rule Language (SWRL rules) that are specially designed for predicting diseases. The prediction process starts by filling data about the appeared signs and symptoms by the user which are after that mapped with the CGDO ontology. Once the data are mapped, the prediction results are presented. The phase of prediction executes the rules which extract the predicted disease details based on the SWRL rule specified. The motivation behind the development of this system is to spread knowledge about the children diseases and their symptoms in a very simple way using the specialized social networking website www.emama.mk.

PMID: 27071879 [PubMed - indexed for MEDLINE]

Web Video Event Recognition by Semantic Analysis from Ubiquitous Documents.

IEEE Trans Image Process. 2016 Sep 27;:

Authors: Yu L, Yang Y, Huang Z, Wang P, Song J, Shen H

Abstract
In recent years, the task of event recognition from videos has attracted increasing interest in multimedia area. While most of the existing research was mainly focused on exploring visual cues to handle relatively small-granular events, it is difficult to directly analyse video content without any prior knowledge. Therefore, synthesizing both the visual and semantic analysis is a natural way for video event understanding. In this paper, we study the problem of web video event recognition, where web videos often describe largegranular events and carry limited textual information. Key challenges include how to accurately represent event semantics from incomplete textual information and how to effectively explore the correlation between visual and textual cues for video event understanding. We propose a novel framework to perform complex event recognition from web videos. In order to compensate the insufficient expressive power of visual cues, we construct an event knowledge base by deeply mining semantic information from ubiquitous web documents. This event knowledge base is capable of describing each event with comprehensive semantics. By utilizing this base, the textual cues for a video can be significantly enriched. Furthermore, we introduce a two-view adaptive regression model which explores the intrinsic correlation between the visual and textual cues of the videos to learn reliable classifiers. Extensive experiments on two real-world video datasets show the effectiveness of our proposed framework and prove that the event knowledge base indeed helps improve the performance of web video event recognition.

PMID: 28114069 [PubMed - as supplied by publisher]

ontologyX: a suite of R packages for working with ontological data.

Bioinformatics. 2017 Jan 05;:

Authors: Greene D, Richardson S, Turro E

Abstract
Ontologies are widely used constructs for encoding and analyzing biomedical data, but the absence of simple and consistent tools has made exploratory and systematic analysis of such data unnecessarily difficult. Here we present three packages which aim to simplify such procedures. The ontologyIndex package enables arbitrary ontologies to be read into R, supports representation of ontological objects by native R types, and provides a parsimonius set of performant functions for querying ontologies. ontologySimilarity and ontologyPlot extend ontologyIndex with functionality for straightforward visualization and semantic similarity calculations, including statistical routines.
AVAILABILITY AND IMPLEMENTATION: ontologyIndex, ontologyPlot and ontologySimilarity are all available on the Comprehensive R Archive Network website under https://cran.r-project.org/web/packages/ CONTACT: Daniel Greene dg333@cam.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

PMID: 28062448 [PubMed - as supplied by publisher]

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation.

IEEE Trans Pattern Anal Mach Intell. 2017 Jan 02;:

Authors: Badrinarayanan V, Kendall A, Cipolla R

Abstract
We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1]. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3], DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/.

PMID: 28060704 [PubMed - as supplied by publisher]

An "integrated health neighbourhood" framework to optimise the use of EHR data.

J Innov Health Inform. 2016 Oct 04;23(3):826

Authors: Liaw ST, De Lusignan S

Abstract
 General practice should become the hub of integrated health neighbourhoods (IHNs), which involves sharing of information to ensure that medical homes are also part of learning organisations that use electronic health record (EHR) data for care, decision making, teaching and learning, quality improvement and research. The IHN is defined as the primary and ambulatory care services in a locality that relates largely to a single hospital-based secondary care service provider and is the logical denominator and unit of comparison for the optimal use of EHR data and health information exchange (HIE) to facilitate integration and coordination of care. Its size may vary based on the geography and requirements of the population, for example between city, suburban and rural areas. The conceptual framework includes context; integration of data, information and knowledge; integration of clinical workflow and practice; and inter-professional integration to ensure coordinated shared care to deliver safe and effective services that are equitable, accessible and culturally respectful. We illustrate how this HIE-supported IHN vision may be achieved with an Australian case study demonstrating the integration of linked pseudonymised records with knowledge- and evidence-based guidelines using semantic web tools and informatics-based methods, researching causal links bewteen data quality and quality of care and the key issues to address. The data presented in this paper form part of the evaluation of the informatics infrastructure - HIE and data repository - for its reliability and utility in supporting the IHN. An IHN can only be created if the necessary health informatics infrastructure is put in place. Integrated care may struggle to be effective without HIE.

PMID: 28059689 [PubMed - in process]

SemanticSCo: a Platform to Support the Semantic Composition of Services for Gene Expression Analysis.

J Biomed Inform. 2017 Jan 02;:

Authors: Guardia GD, Pires LF, da Silva EG, de Farias CR

Abstract
Gene expression studies often require the combined use of a number of analysis tools. However, manual integration of analysis tools can be cumbersome and error prone. To support a higher level of automation in the integration process, efforts have been made in the biomedical domain towards the development of semantic web services and supporting composition environments. Yet, most environments consider only the execution of simple service behaviours and requires users to focus on technical details of the composition process. We propose a novel approach to the semantic composition of gene expression analysis services that addresses the shortcomings of the existing solutions. Our approach includes an architecture designed to support the service composition process for gene expression analysis, and a flexible strategy for the (semi) automatic composition of semantic web services. Finally, we implement a supporting platform called SemanticSCo to realize the proposed composition approach and demonstrate its functionality by successfully reproducing a microarray study documented in the literature. The SemanticSCo platform provides support for the composition of RESTful web services semantically annotated using SAWSDL. Our platform also supports the definition of constraints/conditions regarding the order in which service operations should be invoked, thus enabling the definition of complex service behaviours. Our proposed solution for semantic web service composition takes into account the requirements of different stakeholders and addresses all phases of the service composition process. It also provides support for the definition of analysis workflows at a high-level of abstraction, thus enabling users to focus on biological research issues rather than on the technical details of the composition process. The SemanticSCo source code is available at https://github.com/usplssb/SemanticSCo.

PMID: 28057566 [PubMed - as supplied by publisher]

Ontology construction and application in practice case study of health tourism in Thailand.

Springerplus. 2016;5(1):2106

Authors: Chantrapornchai C, Choksuchat C

Abstract
Ontology is one of the key components in semantic webs. It contains the core knowledge for an effective search. However, building ontology requires the carefully-collected knowledge which is very domain-sensitive. In this work, we present the practice of ontology construction for a case study of health tourism in Thailand. The whole process follows the METHONTOLOGY approach, which consists of phases: information gathering, corpus study, ontology engineering, evaluation, publishing, and the application construction. Different sources of data such as structure web documents like HTML and other documents are acquired in the information gathering process. The tourism corpora from various tourism texts and standards are explored. The ontology is evaluated in two aspects: automatic reasoning using Pellet, and RacerPro, and the questionnaires, used to evaluate by experts of the domains: tourism domain experts and ontology experts. The ontology usability is demonstrated via the semantic web application and via example axioms. The developed ontology is actually the first health tourism ontology in Thailand with the published application.

PMID: 28053835 [PubMed - in process]

RegenBase: a knowledge base of spinal cord injury biology for translational research.

Database (Oxford). 2016;2016:

Authors: Callahan A, Abeyruwan SW, Al-Ali H, Sakurai K, Ferguson AR, Popovich PG, Shah NH, Visser U, Bixby JL, Lemmon VP

Abstract
Spinal cord injury (SCI) research is a data-rich field that aims to identify the biological mechanisms resulting in loss of function and mobility after SCI, as well as develop therapies that promote recovery after injury. SCI experimental methods, data and domain knowledge are locked in the largely unstructured text of scientific publications, making large scale integration with existing bioinformatics resources and subsequent analysis infeasible. The lack of standard reporting for experiment variables and results also makes experiment replicability a significant challenge. To address these challenges, we have developed RegenBase, a knowledge base of SCI biology. RegenBase integrates curated literature-sourced facts and experimental details, raw assay data profiling the effect of compounds on enzyme activity and cell growth, and structured SCI domain knowledge in the form of the first ontology for SCI, using Semantic Web representation languages and frameworks. RegenBase uses consistent identifier schemes and data representations that enable automated linking among RegenBase statements and also to other biological databases and electronic resources. By querying RegenBase, we have identified novel biological hypotheses linking the effects of perturbagens to observed behavioral outcomes after SCI. RegenBase is publicly available for browsing, querying and download.Database URL:http://regenbase.org.

PMID: 27055827 [PubMed - indexed for MEDLINE]

Integration and Querying of Genomic and Proteomic Semantic Annotations for Biomedical Knowledge Extraction.

IEEE/ACM Trans Comput Biol Bioinform. 2016 Mar-Apr;13(2):209-19

Authors: Masseroli M, Canakoglu A, Ceri S

Abstract
Understanding complex biological phenomena involves answering complex biomedical questions on multiple biomolecular information simultaneously, which are expressed through multiple genomic and proteomic semantic annotations scattered in many distributed and heterogeneous data sources; such heterogeneity and dispersion hamper the biologists' ability of asking global queries and performing global evaluations. To overcome this problem, we developed a software architecture to create and maintain a Genomic and Proteomic Knowledge Base (GPKB), which integrates several of the most relevant sources of such dispersed information (including Entrez Gene, UniProt, IntAct, Expasy Enzyme, GO, GOA, BioCyc, KEGG, Reactome, and OMIM). Our solution is general, as it uses a flexible, modular, and multilevel global data schema based on abstraction and generalization of integrated data features, and a set of automatic procedures for easing data integration and maintenance, also when the integrated data sources evolve in data content, structure, and number. These procedures also assure consistency, quality, and provenance tracking of all integrated data, and perform the semantic closure of the hierarchical relationships of the integrated biomedical ontologies. At http://www.bioinformatics.deib.polimi.it/GPKB/, a Web interface allows graphical easy composition of queries, although complex, on the knowledge base, supporting also semantic query expansion and comprehensive explorative search of the integrated data to better sustain biomedical knowledge extraction.

PMID: 27045824 [PubMed - indexed for MEDLINE]

A knowledgebase of the human Alu repetitive elements.

J Biomed Inform. 2016 Apr;60:77-83

Authors: Mallona I, Jordà M, Peinado MA

Abstract
Alu elements are the most abundant retrotransposons in the human genome with more than one million copies. Alu repeats have been reported to participate in multiple processes related with genome regulation and compartmentalization. Moreover, they have been involved in the facilitation of pathological mutations in many diseases, including cancer. The contribution of Alus and other repeats in genomic regulation is often overlooked because their study poses technical and analytical challenges hardly attainable with conventional strategies. Here we propose the integration of ontology-based semantic methods to query a knowledgebase for the human Alus. The knowledgebase for the human Alus leverages Sequence (SO) and Gene Ontologies (GO) and is devoted to address functional and genetic information in the genomic context of the Alus. For each Alu element, the closest gene and transcript are stored, as well their functional annotation according to GO, the state of the chromatin and the transcription factors binding sites inside the Alu. The model uses Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). As a case of use and to illustrate the utility of the tool, we have evaluated the epigenetic states of Alu repeats associated with gene promoters according to their transcriptional activity. The ontology is easily extendable, offering a scaffold for the inclusion of new experimental data. The RDF/XML formalization is freely available at http://aluontology.sourceforge.net/.

PMID: 26827622 [PubMed - indexed for MEDLINE]

Design and Development of a Sharable Clinical Decision Support System Based on a Semantic Web Service Framework.

J Med Syst. 2016 May;40(5):118

Authors: Zhang YF, Gou L, Tian Y, Li TC, Zhang M, Li JS

Abstract
Clinical decision support (CDS) systems provide clinicians and other health care stakeholders with patient-specific assessments or recommendations to aid in the clinical decision-making process. Despite their demonstrated potential for improving health care quality, the widespread availability of CDS systems has been limited mainly by the difficulty and cost of sharing CDS knowledge among heterogeneous healthcare information systems. The purpose of this study was to design and develop a sharable clinical decision support (S-CDS) system that meets this challenge. The fundamental knowledge base consists of independent and reusable knowledge modules (KMs) to meet core CDS needs, wherein each KM is semantically well defined based on the standard information model, terminologies, and representation formalisms. A semantic web service framework was developed to identify, access, and leverage these KMs across diverse CDS applications and care settings. The S-CDS system has been validated in two distinct client CDS applications. Model-level evaluation results confirmed coherent knowledge representation. Application-level evaluation results reached an overall accuracy of 98.66 % and a completeness of 96.98 %. The evaluation results demonstrated the technical feasibility and application prospect of our approach. Compared with other CDS engineering efforts, our approach facilitates system development and implementation and improves system maintainability, scalability and efficiency, which contribute to the widespread adoption of effective CDS within the healthcare domain.

PMID: 27002818 [PubMed - indexed for MEDLINE]

Easy Extraction of Terms and Definitions with OWL2TL.

CEUR Workshop Proc. 2016 Aug;1747:

Authors: Judkins J, Utecht J, Brochhausen M

Abstract
Facilitating good communication between semantic web specialists and domain experts is necessary to efficient ontology development. This development may be hindered by the fact that domain experts tend to be unfamiliar with tools used to create and edit OWL files. This is true in particular when changes to definitions need to be reviewed as often as multiple times a day. We developed "OWL to Term List" (OWL2TL) with the goal of allowing domain experts to view the terms and definitions of an OWL file organized in a list that is updated each time the OWL file is updated. The tool is available online and currently generates a list of terms, along with additional annotation properties that are chosen by the user, in a format that allows easy copying into a spreadsheet.

PMID: 28035214 [PubMed]

Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain.

Comput Methods Programs Biomed. 2016 May;128:52-68

Authors: Madkour M, Benhaddou D, Tao C

Abstract
BACKGROUND AND OBJECTIVE: We live our lives by the calendar and the clock, but time is also an abstraction, even an illusion. The sense of time can be both domain-specific and complex, and is often left implicit, requiring significant domain knowledge to accurately recognize and harness. In the clinical domain, the momentum gained from recent advances in infrastructure and governance practices has enabled the collection of tremendous amount of data at each moment in time. Electronic health records (EHRs) have paved the way to making these data available for practitioners and researchers. However, temporal data representation, normalization, extraction and reasoning are very important in order to mine such massive data and therefore for constructing the clinical timeline. The objective of this work is to provide an overview of the problem of constructing a timeline at the clinical point of care and to summarize the state-of-the-art in processing temporal information of clinical narratives.
METHODS: This review surveys the methods used in three important area: modeling and representing of time, medical NLP methods for extracting time, and methods of time reasoning and processing. The review emphasis on the current existing gap between present methods and the semantic web technologies and catch up with the possible combinations.
RESULTS: The main findings of this review are revealing the importance of time processing not only in constructing timelines and clinical decision support systems but also as a vital component of EHR data models and operations.
CONCLUSIONS: Extracting temporal information in clinical narratives is a challenging task. The inclusion of ontologies and semantic web will lead to better assessment of the annotation task and, together with medical NLP techniques, will help resolving granularity and co-reference resolution problems.

PMID: 27040831 [PubMed - indexed for MEDLINE]

Drug-drug interaction discovery and demystification using Semantic Web technologies.

J Am Med Inform Assoc. 2016 Dec 28;:

Authors: Noor A, Assiri A, Ayvaz S, Clark C, Dumontier M

Abstract
OBJECTIVE: To develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs.
MATERIALS AND METHODS: A mechanism-based DDI knowledge base was constructed by integrating knowledge from several existing sources at the pharmacokinetic, pharmacodynamic, pharmacogenetic, and multipathway interaction levels. A query-based framework was then created to utilize this integrated knowledge base in conjunction with 9 inference rules to infer mechanistic explanations for asserted DDIs and deduce potential DDIs.
RESULTS: The drug-drug interactions discovery and demystification (D3) system achieved an overall 85% recall rate in terms of inferring mechanistic explanations for the DDIs integrated into its knowledge base, while demonstrating a 61% precision rate in terms of the inference or lack of inference of mechanistic explanations for a balanced, randomly selected collection of interacting and noninteracting drug pairs.
DISCUSSION: The successful demonstration of the D3 system's ability to confirm interactions involving well-studied drugs enhances confidence in its ability to deduce interactions involving less-studied drugs. In its demonstration, the D3 system infers putative explanations for most of its integrated DDIs. Further enhancements to this work in the future might include ranking interaction mechanisms based on likelihood of applicability, determining the likelihood of deduced DDIs, and making the framework publicly available.
CONCLUSION: The D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy.

PMID: 28031284 [PubMed - as supplied by publisher]

Semantic Dementia: A Mini-Review.

Mini Rev Med Chem. 2016 Dec 23;

Authors: Klimova B, Novotny M, Kuca K

Abstract
BACKGROUND: At present there are about 47.5 million people suffering from different types of dementia and by 2030 this number should reach 75.6 million. This obviously brings about serious social and economic burden for people suffering from any kind of dementia.
OBJECTIVE: The purpose of this article is to explore only semantic dementia (SD) as one of the forms of frontotemporal dementia (FTD) and provide the latest information on its diagnosis and treatment which play a significant role in the maintenance of quality of life of both patients and their caregivers. Especially unimpaired communication is one of the key factors in the relationship between the patients and their caregivers.
METHODS: The methods used for this mini review include a method of literature review of available sources found in the world's acknowledged databases such as Web of Science, PubMed, Springer and Scopus from the period of 2010 up to the present time; and a method of comparison and evaluation of the selected studies.
RESULTS: The findings of this mini review show that FTD, respectively SD, is a serious neurodegenerative disorder which has fatal consequences for the affected patients. In addition, the findings also indicate that there are not many possibilities of pharmacological treatment for semantic dementia and therefore more attention should be paid to alternative, non-pharmacological approaches.
CONCLUSION: Although semantic dementia is a relatively rare neurodegenerative disorder if compared with other types of dementia, it has an irreversible impact on patient's and his/her caregiver's life in terms of quality.

PMID: 28019640 [PubMed - as supplied by publisher]

Computational modeling of brain pathologies: the case of multiple sclerosis.

Brief Bioinform. 2016 Dec 22;:

Authors: Pappalardo F, Rajput AM, Motta S

Abstract
The central nervous system is the most complex network of the human body. The existence and functionality of a large number of molecular species in human brain are still ambiguous and mostly unknown, thus posing a challenge to Science and Medicine. Neurological diseases inherit the same level of complexity, making effective treatments difficult to be found. Multiple sclerosis (MS) is a major neurological disease that causes severe inabilities and also a significant social burden on health care system: between 2 and 2.5 million people are affected by it, and the cost associated with it is significantly higher as compared with other neurological diseases because of the chronic nature of the disease and to the partial efficacy of current therapies. Despite difficulties in understanding and treating MS, many computational models have been developed to help neurologists. In the present work, we briefly review the main characteristics of MS and present a selection criteria of modeling approaches.

PMID: 28011755 [PubMed - as supplied by publisher]

A Computational Chemistry Data Management Platform Based on the Semantic Web.

J Phys Chem A. 2016 Dec 12;

Authors: Wang B, Dobosh PA, Chalk SJ, Sopek M, Ostlund NS

Abstract
This paper presents a formal data publishing platform for computational chemistry using semantic web technologies. This platform encapsulates computational chemistry data from a variety of packages in an Extensible Markup Language (XML) file called CSX (Common Standard for eXchange). Based on a Gainesville Core (GC) ontology for computational chemistry, the CSX XML file is converted into the JavaScript Object Notation for Linked Data (JSON-LD) format using an XML Stylesheet Language Transformation (XSLT) file. Ultimately the JSON-LD file is converted to subject-predicate-object triples in a Turtle (TTL) file and published on the web portal. By leveraging semantic web technologies, we are able to place computational chemistry data onto web portals as a component of a Giant Global Graph (GGG) such that computer agents, as well as individual chemists, can access the data.

PMID: 27936706 [PubMed - as supplied by publisher]

LifeWatch Greece data-services: Discovering Biodiversity Data using Semantic Web Technologies.

Biodivers Data J. 2016;(4):e8443

Authors: Minadakis N, Marketakis Y, Doerr M, Bekiari C, Papadakos P, Gougousis A, Bailly N, Arvanitidis C

Abstract
BACKGROUND: Biodiversity data is characterized by its cross-disciplinary character, the extremely broad range of data types and structures, and the variety of semantic concepts that it encompasses. Furthermore there is a plethora of different data sources providing resources for the same piece of information in a heterogeneous way. Even if we restrict our attention to Greek biodiversity domain, it is easy to see that biodiversity data remains unconnected and widely distributed among different sources.
NEW INFORMATION: To cope with these issues, in the context of the LifeWatch Greece project, i) we supported cataloguing and publishing of all the relevant metadata information of the Greek biodiversity domain, ii) we integrated data from heterogeneous sources by supporting the definitions of appropriate models, iii) we provided means for efficiently discovering biodiversity data of interest and iv) we enabled the answering of complex queries that could not be answered from the individual sources. This work has been exploited, evaluated and scientificaly confirmed by the biodiversity community through the services provided by the LifeWatch Greece portal.

PMID: 27932908 [PubMed]

Publication of nuclear magnetic resonance experimental data with semantic web technology and the application thereof to biomedical research of proteins.

J Biomed Semantics. 2016 May 05;7(1):16

Authors: Yokochi M, Kobayashi N, Ulrich EL, Kinjo AR, Iwata T, Ioannidis YE, Livny M, Markley JL, Nakamura H, Kojima C, Fujiwara T

Abstract
BACKGROUND: The nuclear magnetic resonance (NMR) spectroscopic data for biological macromolecules archived at the BioMagResBank (BMRB) provide a rich resource of biophysical information at atomic resolution. The NMR data archived in NMR-STAR ASCII format have been implemented in a relational database. However, it is still fairly difficult for users to retrieve data from the NMR-STAR files or the relational database in association with data from other biological databases.
FINDINGS: To enhance the interoperability of the BMRB database, we present a full conversion of BMRB entries to two standard structured data formats, XML and RDF, as common open representations of the NMR-STAR data. Moreover, a SPARQL endpoint has been deployed. The described case study demonstrates that a simple query of the SPARQL endpoints of the BMRB, UniProt, and Online Mendelian Inheritance in Man (OMIM), can be used in NMR and structure-based analysis of proteins combined with information of single nucleotide polymorphisms (SNPs) and their phenotypes.
CONCLUSIONS: We have developed BMRB/XML and BMRB/RDF and demonstrate their use in performing a federated SPARQL query linking the BMRB to other databases through standard semantic web technologies. This will facilitate data exchange across diverse information resources.

PMID: 27927232 [PubMed - in process]

DNA Data Bank of Japan.

Nucleic Acids Res. 2016 Oct 24;:

Authors: Mashima J, Kodama Y, Fujisawa T, Katayama T, Okuda Y, Kaminuma E, Ogasawara O, Okubo K, Nakamura Y, Takagi T

Abstract
The DNA Data Bank of Japan (DDBJ) (http://www.ddbj.nig.ac.jp) has been providing public data services for thirty years (since 1987). We are collecting nucleotide sequence data from researchers as a member of the International Nucleotide Sequence Database Collaboration (INSDC, http://www.insdc.org), in collaboration with the US National Center for Biotechnology Information (NCBI) and European Bioinformatics Institute (EBI). The DDBJ Center also services Japanese Genotype-phenotype Archive (JGA), with the National Bioscience Database Center to collect human-subjected data from Japanese researchers. Here, we report our database activities for INSDC and JGA over the past year, and introduce retrieval and analytical services running on our supercomputer system and their recent modifications. Furthermore, with the Database Center for Life Science, the DDBJ Center improves semantic web technologies to integrate and to share biological data, for providing the RDF version of the sequence data.

PMID: 27924010 [PubMed - as supplied by publisher]