SCALEUS: Semantic Web Services Integration for Biomedical Applications.

J Med Syst. 2017 Apr;41(4):54

Authors: Sernadela P, González-Castro L, Oliveira JL

Abstract
In recent years, we have witnessed an explosion of biological data resulting largely from the demands of life science research. The vast majority of these data are freely available via diverse bioinformatics platforms, including relational databases and conventional keyword search applications. This type of approach has achieved great results in the last few years, but proved to be unfeasible when information needs to be combined or shared among different and scattered sources. During recent years, many of these data distribution challenges have been solved with the adoption of semantic web. Despite the evident benefits of this technology, its adoption introduced new challenges related with the migration process, from existent systems to the semantic level. To facilitate this transition, we have developed Scaleus, a semantic web migration tool that can be deployed on top of traditional systems in order to bring knowledge, inference rules, and query federation to the existent data. Targeted at the biomedical domain, this web-based platform offers, in a single package, straightforward data integration and semantic web services that help developers and researchers in the creation process of new semantically enhanced information systems. SCALEUS is available as open source at http://bioinformatics-ua.github.io/scaleus/ .

PMID: 28214993 [PubMed - in process]

Modeling and Validating HL7 FHIR Profiles Using Semantic Web Shape Expressions (ShEx).

J Biomed Inform. 2017 Feb 14;:

Authors: Solbrig HR, Prud'hommeaux E, Grieve G, McKenzie L, Mandel JC, Sharma DK, Jiang G

Abstract
BACKGROUND: HL7 Fast Healthcare Interoperability Resources (FHIR) is an emerging open standard for the exchange of electronic healthcare information. FHIR resources are defined in a specialized modeling language. FHIR instances can currently be represented in either XML or JSON. The FHIR and Semantic Web communities are developing a third FHIR instance representation format in Resource Description Framework (RDF). Shape Expressions (ShEx), a formal RDF data constraint language, is a candidate for describing and validating the FHIR RDF representation.
OBJECTIVE: Create a FHIR to ShEx model transformation and assess its ability to describe and validate FHIR RDF data.
METHODS: We created the methods and tools that generate the ShEx schemas modeling the FHIR to RDF specification being developed by HL7 ITS/W3C RDF Task Force, and evaluated the applicability of ShEx in the description and validation of FHIR to RDF transformations.
RESULTS: The ShEx models contributed significantly to workgroup consensus. Algorithmic transformations from the FHIR model to ShEx schemas and FHIR example data to RDF transformations were incorporated into the FHIR build process. ShEx schemas representing 109 FHIR resources were used to validate 511 FHIR RDF data examples from the Standards for Trial Use (STU 3) Ballot version. We were able to uncover unresolved issues in the FHIR to RDF specification and detect 10 types of errors and root causes in the actual implementation. The FHIR ShEx representations have been included in the official FHIR web pages for the STU 3 Ballot version since September 2016.
DISCUSSION: ShEx can be used to define and validate the syntax of a FHIR resource, which is complementary to the use of RDF Schema (RDFS) and Web Ontology Language (OWL) for semantic validation.
CONCLUSION: ShEx proved useful for describing a standard model of FHIR RDF data. The combination of a formal model and a succinct format enabled comprehensive review and automated validation.

PMID: 28213144 [PubMed - as supplied by publisher]

Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support.

BioData Min. 2017;10:7

Authors: Mohammadhassanzadeh H, Van Woensel W, Abidi SR, Abidi SS

Abstract
BACKGROUND: Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians' experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.
RESULTS: We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead.
CONCLUSIONS: We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.

PMID: 28203277 [PubMed]

DaTo: an atlas of biological databases and tools.

J Integr Bioinform. 2016 Dec 18;13(4):297

Authors: Li Q, Zhou Y, Jiao Y, Zhang Z, Bai L, Tong L, Yang X, Sommer B, Hofestädt R, Chen M

Abstract
This work presents DaTo, a semi-automatically generated world atlas of biological databases and tools. It extracts raw information from all PubMed articles which contain exact URLs in their abstract section, followed by a manual curation of the abstract and the URL accessibility. DaTo features a user-friendly query interface, providing extensible URL-related annotations, such as the status, the location and the country of the URL. A graphical interaction network browser has also been integrated into the DaTo web interface to facilitate exploration of the relationship between different tools and databases with respect to their ontology-based semantic similarity. Using DaTo, the geographical locations, the health statuses, as well as the journal associations were evaluated with respect to the historical development of bioinformatics tools and databases over the last 20 years. We hope it will inspire the biological community to gain a systematic insight into bioinformatics resources. DaTo is accessible via http://bis.zju.edu.cn/DaTo/.

PMID: 28187413 [PubMed - in process]

SPANG: a SPARQL client supporting generation and reuse of queries for distributed RDF databases.

BMC Bioinformatics. 2017 Feb 08;18(1):93

Authors: Chiba H, Uchiyama I

Abstract
BACKGROUND: Toward improved interoperability of distributed biological databases, an increasing number of datasets have been published in the standardized Resource Description Framework (RDF). Although the powerful SPARQL Protocol and RDF Query Language (SPARQL) provides a basis for exploiting RDF databases, writing SPARQL code is burdensome for users including bioinformaticians. Thus, an easy-to-use interface is necessary.
RESULTS: We developed SPANG, a SPARQL client that has unique features for querying RDF datasets. SPANG dynamically generates typical SPARQL queries according to specified arguments. It can also call SPARQL template libraries constructed in a local system or published on the Web. Further, it enables combinatorial execution of multiple queries, each with a distinct target database. These features facilitate easy and effective access to RDF datasets and integrative analysis of distributed data.
CONCLUSIONS: SPANG helps users to exploit RDF datasets by generation and reuse of SPARQL queries through a simple interface. This client will enhance integrative exploitation of biological RDF datasets distributed across the Web. This software package is freely available at http://purl.org/net/spang .

PMID: 28178937 [PubMed - in process]

An improved method for functional similarity analysis of genes based on Gene Ontology.

BMC Syst Biol. 2016 Dec 23;10(Suppl 4):119

Authors: Tian Z, Wang C, Guo M, Liu X, Teng Z

Abstract
BACKGROUND: Measures of gene functional similarity are essential tools for gene clustering, gene function prediction, evaluation of protein-protein interaction, disease gene prioritization and other applications. In recent years, many gene functional similarity methods have been proposed based on the semantic similarity of GO terms. However, these leading approaches may make errorprone judgments especially when they measure the specificity of GO terms as well as the IC of a term set. Therefore, how to estimate the gene functional similarity reliably is still a challenging problem.
RESULTS: We propose WIS, an effective method to measure the gene functional similarity. First of all, WIS computes the IC of a term by employing its depth, the number of its ancestors as well as the topology of its descendants in the GO graph. Secondly, WIS calculates the IC of a term set by means of considering the weighted inherited semantics of terms. Finally, WIS estimates the gene functional similarity based on the IC overlap ratio of term sets. WIS is superior to some other representative measures on the experiments of functional classification of genes in a biological pathway, collaborative evaluation of GO-based semantic similarity measures, protein-protein interaction prediction and correlation with gene expression. Further analysis suggests that WIS takes fully into account the specificity of terms and the weighted inherited semantics of terms between GO terms.
CONCLUSIONS: The proposed WIS method is an effective and reliable way to compare gene function. The web service of WIS is freely available at http://nclab.hit.edu.cn/WIS/ .

PMID: 28155727 [PubMed - in process]

Mobile Technology in the Perioperative Arena: Rapid Evolution and Future Disruption.

Anesth Analg. 2017 Feb 01;:

Authors: Rothman BS, Gupta RK, McEvoy MD

Abstract
Throughout the history of medicine, physicians have relied upon disruptive innovations and technologies to improve the quality of care delivered, patient outcomes, and patient satisfaction. The implementation of mobile technology in health care is quickly becoming the next disruptive technology. We first review the history of mobile technology over the past 3 decades, discuss the impact of hardware and software, explore the rapid expansion of applications (apps), and evaluate the adoption of mobile technology in health care. Next, we discuss how technology serves as the vehicle that can transform traditional didactic learning into one that adapts to the learning behavior of the student by using concepts such as the flipped classroom, just-in-time learning, social media, and Web 2.0/3.0. The focus in this modern education paradigm is shifting from teacher-centric to learner-centric, including providers and patients, and is being delivered as context-sensitive, or semantic, learning. Finally, we present the methods by which connected health systems via mobile devices increase information collection and analysis from patients in both clinical care and research environments. This enhanced patient and provider connection has demonstrated benefits including reducing unnecessary hospital readmissions, improved perioperative health maintenance coordination, and improved care in remote and underserved areas. A significant portion of the future of health care, and specifically perioperative medicine, revolves around mobile technology, nimble learners, patient-specific information and decision-making, and continuous connectivity between patients and health care systems. As such, an understanding of developing or evaluating mobile technology likely will be important for anesthesiologists, particularly with an ever-expanding scope of practice in perioperative medicine.

PMID: 28151816 [PubMed - as supplied by publisher]

Genes2GO: A web application for querying gene sets for specific GO terms.

Bioinformation. 2016;12(3):231-232

Authors: Chawla K, Kuiper M

Abstract
Gene ontology annotations have become an essential resource for biological interpretations of experimental findings. The process of gathering basic annotation information in tables that link gene sets with specific gene ontology terms can be cumbersome, in particular if it requires above average computer skills or bioinformatics expertise. We have therefore developed Genes2GO, an intuitive R-based web application. Genes2GO uses the biomaRt package of Bioconductor in order to retrieve custom sets of gene ontology annotations for any list of genes from organisms covered by the Ensembl database. Genes2GO produces a binary matrix file, indicating for each gene the presence or absence of specific annotations for a gene. It should be noted that other GO tools do not offer this user-friendly access to annotations.
AVAILABILITY: Genes2GO is freely available and listed under http://www.semantic-systems-biology.org/tools/externaltools/.

PMID: 28149059 [PubMed]

SAFE: SPARQL Federation over RDF Data Cubes with Access Control.

J Biomed Semantics. 2017 Feb 01;8(1):5

Authors: Khan Y, Saleem M, Mehdi M, Hogan A, Mehmood Q, Rebholz-Schuhmann D, Sahay R

Abstract
BACKGROUND: Several query federation engines have been proposed for accessing public Linked Open Data sources. However, in many domains, resources are sensitive and access to these resources is tightly controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through access control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data.
RESULTS: We present SAFE: a query federation engine that enables policy-aware access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where access control is coupled with source selection, user profiles and their access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter access control, SAFE's indexing system does not hold any data instances-it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change.
CONCLUSIONS: We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-level access control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

PMID: 28148277 [PubMed - in process]

Toward cognitive pipelines of medical assistance algorithms.

Int J Comput Assist Radiol Surg. 2016 Sep;11(9):1743-53

Authors: Philipp P, Maleshkova M, Katic D, Weber C, Götz M, Rettinger A, Speidel S, Kämpgen B, Nolden M, Wekerle AL, Dillmann R, Kenngott H, Müller B, Studer R

Abstract
PURPOSE: Assistance algorithms for medical tasks have great potential to support physicians with their daily work. However, medicine is also one of the most demanding domains for computer-based support systems, since medical assistance tasks are complex and the practical experience of the physician is crucial. Recent developments in the area of cognitive computing appear to be well suited to tackle medicine as an application domain.
METHODS: We propose a system based on the idea of cognitive computing and consisting of auto-configurable medical assistance algorithms and their self-adapting combination. The system enables automatic execution of new algorithms, given they are made available as Medical Cognitive Apps and are registered in a central semantic repository. Learning components can be added to the system to optimize the results in the cases when numerous Medical Cognitive Apps are available for the same task. Our prototypical implementation is applied to the areas of surgical phase recognition based on sensor data and image progressing for tumor progression mappings.
RESULTS: Our results suggest that such assistance algorithms can be automatically configured in execution pipelines, candidate results can be automatically scored and combined, and the system can learn from experience. Furthermore, our evaluation shows that the Medical Cognitive Apps are providing the correct results as they did for local execution and run in a reasonable amount of time.
CONCLUSION: The proposed solution is applicable to a variety of medical use cases and effectively supports the automated and self-adaptive configuration of cognitive pipelines based on medical interpretation algorithms.

PMID: 26646415 [PubMed - indexed for MEDLINE]

Vigi4Med Scraper: A Framework for Web Forum Structured Data Extraction and Semantic Representation.

PLoS One. 2017;12(1):e0169658

Authors: Audeh B, Beigbeder M, Zimmermann A, Jaillon P, Bousquet C

Abstract
The extraction of information from social media is an essential yet complicated step for data analysis in multiple domains. In this paper, we present Vigi4Med Scraper, a generic open source framework for extracting structured data from web forums. Our framework is highly configurable; using a configuration file, the user can freely choose the data to extract from any web forum. The extracted data are anonymized and represented in a semantic structure using Resource Description Framework (RDF) graphs. This representation enables efficient manipulation by data analysis algorithms and allows the collected data to be directly linked to any existing semantic resource. To avoid server overload, an integrated proxy with caching functionality imposes a minimal delay between sequential requests. Vigi4Med Scraper represents the first step of Vigi4Med, a project to detect adverse drug reactions (ADRs) from social networks founded by the French drug safety agency Agence Nationale de Sécurité du Médicament (ANSM). Vigi4Med Scraper has successfully extracted greater than 200 gigabytes of data from the web forums of over 20 different websites.

PMID: 28122056 [PubMed - in process]

Generalization and maintenance of treatment gains in primary progressive aphasia (PPA): a systematic review.

Int J Lang Commun Disord. 2017 Jan 24;:

Authors: Cadório I, Lousada M, Martins P, Figueiredo D

Abstract
BACKGROUND: Cognitive-linguistic treatments and interventions targeting communication have been developed within the context of primary progressive aphasia (PPA), however knowledge about the scope of generalization and maintenance of therapy gains considering PPA subtypes remains scarce and awaits systematic investigation.
AIMS: To analyse the effects of semantic therapy on generalization and maintenance of treatment outcomes in individuals with PPA, considering its different subtypes.
METHODS & PROCEDURES: Central, PubMed, Medline, Web of Knowledge and Scopus were used to retrieve articles of interest. A total of 25 non-randomized studies published between 2000 and 2016 met the eligibility criteria and therefore were included in this study.
MAIN CONTRIBUTION: This systematic review provides evidence-based information for clinical practice in PPA. Generalization and maintenance effects post-treatment for each PPA variant are analysed and discussed. Several factors are described as important to maximize the scope for generalization and maintenance of treatment gains.
CONCLUSIONS & IMPLICATIONS: Generalization is particularly hard to achieve in the semantic variant, as in the face of degraded semantic knowledge learning is rigid and context dependent. In contrast, non-fluent and logopenic variants offer better scope for generalization. Maintenance patterns do not seem to be influenced by PPA subtype, but rather by other factors such as continued practice, treatment length and frequency of sessions. In the future, clinicians should consider the PPA subtype when planning the treatment protocol.

PMID: 28120406 [PubMed - as supplied by publisher]

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]