An empirical meta-analysis of the life sciences linked open data on the web.

Sci Data. 2021 Jan 21;8(1):24

Authors: Kamdar MR, Musen MA

Abstract
While the biomedical community has published several "open data" sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 biomedical linked open data sources into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.

PMID: 33479214 [PubMed - in process]

Ethnomedicinal uses, phytochemistry, and biological activity of plants of the genus Gynura.

J Ethnopharmacol. 2021 Jan 16;:113834

Authors: Bari MS, Khandokar L, Haque E, Romano B, Capasso R, Seidel V, Haque MA, Rashid MA

Abstract
ETHNOPHARMACOLOGICAL RELEVANCE: The genus Gynura (Compositae) includes around 46 species and is native to the tropical regions of Southeast Asia, Africa and Australia. Many species within this genus are used in ethnomedicine to treat various disorders including skin diseases, injuries, ulcers, wounds, burns, sores, scalds, as well as for the management of diabetes, hypertension, hyperlipidemia, constipation, rheumatism, bronchitis and inflammation.
AIM OF THE REVIEW: This review is an attempt to provide scientific information regarding the ethnopharmacology, phytochemistry, pharmacological and toxicological profiles of Gynura species along with the nomenclature, distribution, taxonomy and botanical features of the genus. A critical analysis has been undertaken to understand the current and future pharmaceutical prospects of the genus.
MATERIALS & METHODS: Several electronic databases, including Google scholar, PubMed, Web of Science, Scopus, ScienceDirect, SpringerLink, Semantic Scholar, MEDLINE and CNKI Scholar, were explored as information sources. The Plant List Index was used for taxonomical authentications. SciFinder and PubChem assisted in the verification of chemical structures.
RESULTS: A large number of phytochemical analyses on Gynura have revealed the presence of around 342 phytoconstituents including pyrrolizidine alkaloids, phenolic compounds, chromanones, phenylpropanoid glycosides, flavonoids, flavonoid glycosides, steroids, steroidal glycosides, cerebrosides, carotenoids, triterpenes, mono- and sesquiterpenes, norisoprenoids, oligosaccharides, polysaccharides and proteins. Several in vitro and in vivo studies have demonstrated the pharmacological potential of Gynura species, including antidiabetic, anti-oxidant, anti-inflammatory, antimicrobial, antihypertensive and anticancer activities. Although the presence of pyrrolizidine alkaloids within a few species has been associated with possible hepatotoxicity, most of the common species have a good safety profile.
CONCLUSIONS: The importance of the genus Gynura both as a prominent contributor in ethnomedicinal systems as well as a source of promising bioactive molecules is evident. Only about one fourth of Gynura species have been studied so far. This review aims to provide some scientific basis for future endeavors, including in-depth biological and chemical investigations into already studied species as well as other lesser known species of Gynura.

PMID: 33465439 [PubMed - as supplied by publisher]

Large-scale regulatory and signaling network assembly through linked open data.

Database (Oxford). 2021 Jan 18;2021:

Authors: Lefebvre M, Gaignard A, Folschette M, Bourdon J, Guziolowski C

Abstract
Huge efforts are currently underway to address the organization of biological knowledge through linked open databases. These databases can be automatically queried to reconstruct regulatory and signaling networks. However, assembling networks implies manual operations due to source-specific identification of biological entities and relationships, multiple life-science databases with redundant information and the difficulty of recovering logical flows in biological pathways. We propose a framework based on Semantic Web technologies to automate the reconstruction of large-scale regulatory and signaling networks in the context of tumor cells modeling and drug screening. The proposed tool is pyBRAvo (python Biological netwoRk Assembly), and here we have applied it to a dataset of 910 gene expression measurements issued from liver cancer patients. The tool is publicly available at https://github.com/pyBRAvo/pyBRAvo.

PMID: 33459761 [PubMed - in process]

Establishment and application of information resource of mutant mice in RIKEN BioResource Research Center.

Lab Anim Res. 2021 Jan 18;37(1):6

Authors: Masuya H, Usuda D, Nakata H, Yuhara N, Kurihara K, Namiki Y, Iwase S, Takada T, Tanaka N, Suzuki K, Yamagata Y, Kobayashi N, Yoshiki A, Kushida T

Abstract
Online databases are crucial infrastructures to facilitate the wide effective and efficient use of mouse mutant resources in life sciences. The number and types of mouse resources have been rapidly growing due to the development of genetic modification technology with associated information of genomic sequence and phenotypes. Therefore, data integration technologies to improve the findability, accessibility, interoperability, and reusability of mouse strain data becomes essential for mouse strain repositories. In 2020, the RIKEN BioResource Research Center released an integrated database of bioresources including, experimental mouse strains, Arabidopsis thaliana as a laboratory plant, cell lines, microorganisms, and genetic materials using Resource Description Framework-related technologies. The integrated database shows multiple advanced features for the dissemination of bioresource information. The current version of our online catalog of mouse strains which functions as a part of the integrated database of bioresources is available from search bars on the page of the Center ( https://brc.riken.jp ) and the Experimental Animal Division ( https://mus.brc.riken.jp/ ) websites. The BioResource Research Center also released a genomic variation database of mouse strains established in Japan and Western Europe, MoG+ ( https://molossinus.brc.riken.jp/mogplus/ ), and a database for phenotype-phenotype associations across the mouse phenome using data from the International Mouse Phenotyping Platform. In this review, we describe features of current version of databases related to mouse strain resources in RIKEN BioResource Research Center and discuss future views.

PMID: 33455583 [PubMed]

Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach.

PLoS One. 2021;16(1):e0245264

Authors: Sabah A, Tiun S, Sani NS, Ayob M, Taha AY

Abstract
Existing text clustering methods utilize only one representation at a time (single view), whereas multiple views can represent documents. The multiview multirepresentation method enhances clustering quality. Moreover, existing clustering methods that utilize more than one representation at a time (multiview) use representation with the same nature. Hence, using multiple views that represent data in a different representation with clustering methods is reasonable to create a diverse set of candidate clustering solutions. On this basis, an effective dynamic clustering method must consider combining multiple views of data including semantic view, lexical view (word weighting), and topic view as well as the number of clusters. The main goal of this study is to develop a new method that can improve the performance of web search result clustering (WSRC). An enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method is proposed to create a set of diverse candidate solutions and select a high-quality overlapping cluster. The overlapping clusters are obtained from the candidate solutions created by different clustering methods. The framework to develop the proposed MMCC includes numerous stages: (1) acquiring the standard datasets (MORESQUE and Open Directory Project-239), which are used to validate search result clustering algorithms, (2) preprocessing the dataset, (3) applying multiview multirepresentation clustering models, (4) using the radius-based cluster number estimation algorithm, and (5) employing the consensus clustering ensemble method. Results show an improvement in clustering methods when multiview multirepresentation is used. More importantly, the proposed MMCC model improves the overall performance of WSRC compared with all single-view clustering models.

PMID: 33449949 [PubMed - as supplied by publisher]

Diverse Taxonomies for Diverse Chemistries: Enhanced Representation of Natural Product Metabolism in UniProtKB.

Metabolites. 2021 Jan 12;11(1):

Authors: Feuermann M, Boutet E, Morgat A, Axelsen KB, Bansal P, Bolleman J, de Castro E, Coudert E, Gasteiger E, Géhant S, Lieberherr D, Lombardot T, Neto TB, Pedruzzi I, Poux S, Pozzato M, Redaschi N, Bridge A, On Behalf Of The UniProt Consortium

Abstract
The UniProt Knowledgebase UniProtKB is a comprehensive, high-quality, and freely accessible resource of protein sequences and functional annotation that covers genomes and proteomes from tens of thousands of taxa, including a broad range of plants and microorganisms producing natural products of medical, nutritional, and agronomical interest. Here we describe work that enhances the utility of UniProtKB as a support for both the study of natural products and for their discovery. The foundation of this work is an improved representation of natural product metabolism in UniProtKB using Rhea, an expert-curated knowledgebase of biochemical reactions, that is built on the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Knowledge of natural products and precursors is captured in ChEBI, enzyme-catalyzed reactions in Rhea, and enzymes in UniProtKB/Swiss-Prot, thereby linking chemical structure data directly to protein knowledge. We provide a practical demonstration of how users can search UniProtKB for protein knowledge relevant to natural products through interactive or programmatic queries using metabolite names and synonyms, chemical identifiers, chemical classes, and chemical structures and show how to federate UniProtKB with other data and knowledge resources and tools using semantic web technologies such as RDF and SPARQL. All UniProtKB data are freely available for download in a broad range of formats for users to further mine or exploit as an annotation source, to enrich other natural product datasets and databases.

PMID: 33445429 [PubMed]

A computational approach to predict multi-pathway drug-drug interactions: A case study of irinotecan, a colon cancer medication.

Saudi Pharm J. 2020 Dec;28(12):1507-1513

Authors: Assiri A, Noor A

Abstract
Drug-drug interactions (DDIs) are a potentially distressing corollary of drug interventions, and may result in discomfort, debilitating illness, or even death. Existing research predominantly considers only a single level of interaction; however, serious health complications may result from multi-pathway DDIs, and so new methods are needed to enable predicting and preventing complex DDIs. This article introduces a novel method for the prediction of DDIs at two pharmacological levels (metabolic and transporter interactions) by means of a rule-based model implemented with Semantic Web technologies. The chemotherapy agent irinotecan is used as a case study for demonstrating the validity of this approach. Mechanistic and interaction data were mined from available sources and then used to predict interactors of irinotecan, including potential DDIs mediated by previously unidentified mechanisms. The findings also draw attention to the profound variation between DDI resources, indicating that clinical practice would see significant value from the development of an evidence-based resource to support DDI identification.

PMID: 33424244 [PubMed]

Big data augmentated business trend identification: the case of mobile commerce.

Scientometrics. 2021 Jan 05;:1-27

Authors: Saritas O, Bakhtin P, Kuzminov I, Khabirova E

Abstract
Identifying and monitoring business and technological trends are crucial for innovation and competitiveness of businesses. Exponential growth of data across the world is invaluable for identifying emerging and evolving trends. On the other hand, the vast amount of data leads to information overload and can no longer be adequately processed without the use of automated methods of extraction, processing, and generation of knowledge. There is a growing need for information systems that would monitor and analyse data from heterogeneous and unstructured sources in order to enable timely and evidence-based decision-making. Recent advancements in computing and big data provide enormous opportunities for gathering evidence on future developments and emerging opportunities. The present study demonstrates the use of text-mining and semantic analysis of large amount of documents for investigating in business trends in mobile commerce (m-commerce). Particularly with the on-going COVID-19 pandemic and resultant social isolation, m-commerce has become a large technology and business domain with ever growing market potentials. Thus, our study begins with a review of global challenges, opportunities and trends in the development of m-commerce in the world. Next, the study identifies critical technologies and instruments for the full utilization of the potentials in the sector by using the intelligent big data analytics system based on in-depth natural language processing utilizing text-mining, machine learning, science bibliometry and technology analysis. The results generated by the system can be used to produce a comprehensive and objective web of interconnected technologies, trends, drivers and barriers to give an overview of the whole landscape of m-commerce in one business intelligence (BI) data mart diagram.

PMID: 33424052 [PubMed - as supplied by publisher]

SNAFU: The Semantic Network and Fluency Utility.

Behav Res Methods. 2020 08;52(4):1681-1699

Authors: Zemla JC, Cao K, Mueller KD, Austerweil JL

Abstract
The verbal fluency task-listing words from a category or words that begin with a specific letter-is a common experimental paradigm that is used to diagnose memory impairments and to understand how we store and retrieve knowledge. Data from the verbal fluency task are analyzed in many different ways, often requiring manual coding that is time intensive and error-prone. Researchers have also used fluency data from groups or individuals to estimate semantic networks-latent representations of semantic memory that describe the relations between concepts-that further our understanding of how knowledge is encoded. However computational methods used to estimate networks are not standardized and can be difficult to implement, which has hindered widespread adoption. We present SNAFU: the Semantic Network and Fluency Utility, a tool for estimating networks from fluency data and automatizing traditional fluency analyses, including counting cluster switches and cluster sizes, intrusions, perseverations, and word frequencies. In this manuscript, we provide a primer on using the tool, illustrate its application by creating a semantic network for foods, and validate the tool by comparing results to trained human coders using multiple datasets.

PMID: 32128696 [PubMed - indexed for MEDLINE]

BMJ Open. 2021 Jan 8;11(1):e040808. doi: 10.1136/bmjopen-2020-040808.

ABSTRACT

OBJECTIVES: This study estimates the prevalence of cancers that are categorised as treatable but not curable (TbnC) in England. It provides a quantification of the population and a framework to aid identification of this group to enable the design of tailored support services.

DESIGN: Through consultation with clinical and data experts an algorithmic definition of TbnC was developed. Using cancer registry data sets, with five other linked data sets held by the National Disease Registration Service, the algorithm was applied as part of this retrospective cohort study to estimate the size and characteristics of the TbnC population.

SETTING AND PARTICIPANTS: The health data records of 1.6 million people living with cancer in England in 2015, following a cancer diagnosis between 2001 and 2015, were retrospectively assessed for TbnC status.

RESULTS: An estimated 110 615 people in England were living with TbnC cancer at the end of 2015, following identification of TbnC cancer between 2012 and 2015. In addition, 51 946 people fit the initial search criteria but were found to have been in their last year of life at the end of 2015 and therefore considered separately here as end of life cases. A further 57 117 people in England were initially identified as being at high risk of recurrence or having their life being shortened by cancer but did not fit the TbnC conceptual framework and were excluded, but their results are also reported under 'group B'.

CONCLUSIONS: A population living with TbnC cancer can be identified using data currently collected on a national scale in England. This large population living with TbnC cancer requires personalised treatment and support.

PMID:33419907 | PMC:PMC7798682 | DOI:10.1136/bmjopen-2020-040808

An ontology-based approach for developing a harmonised data-validation tool for European cancer registration.

J Biomed Semantics. 2021 Jan 06;12(1):1

Authors: Nicholson NC, Giusti F, Bettio M, Negrao Carvalho R, Dimitrova N, Dyba T, Flego M, Neamtiu L, Randi G, Martos C

Abstract
BACKGROUND: Population-based cancer registries constitute an important information source in cancer epidemiology. Studies collating and comparing data across regional and national boundaries have proved important for deploying and evaluating effective cancer-control strategies. A critical aspect in correctly comparing cancer indicators across regional and national boundaries lies in ensuring a good and harmonised level of data quality, which is a primary motivator for a centralised collection of pseudonymised data. The recent introduction of the European Union's general data-protection regulation (GDPR) imposes stricter conditions on the collection, processing, and sharing of personal data. It also considers pseudonymised data as personal data. The new regulation motivates the need to find solutions that allow a continuation of the smooth processes leading to harmonised European cancer-registry data. One element in this regard would be the availability of a data-validation software tool based on a formalised depiction of the harmonised data-validation rules, allowing an eventual devolution of the data-validation process to the local level.
RESULTS: A semantic data model was derived from the data-validation rules for harmonising cancer-data variables at European level. The data model was encapsulated in an ontology developed using the Web-Ontology Language (OWL) with the data-model entities forming the main OWL classes. The data-validation rules were added as axioms in the ontology. The reasoning function of the resulting ontology demonstrated its ability to trap registry-coding errors and in some instances to be able to correct errors.
CONCLUSIONS: Describing the European cancer-registry core data set in terms of an OWL ontology affords a tool based on a formalised set of axioms for validating a cancer-registry's data set according to harmonised, supra-national rules. The fact that the data checks are inherently linked to the data model would lead to less maintenance overheads and also allow automatic versioning synchronisation, important for distributed data-quality checking processes.

PMID: 33407816 [PubMed - in process]

A Network Analysis of Research Topics and Trends in End-of-Life Care and Nursing.

Int J Environ Res Public Health. 2021 Jan 04;18(1):

Authors: Kim K, Jang SG, Lee KS

Abstract
This study identified the trends in end-of-life care and nursing through text network analysis. About 18,935 articles published until September 2019 were selected through searches on PubMed, Embase, Cochrane, Web of Science, and Cumulative Index to Nursing and Allied Health Literature. For topic modeling, Latent Dirichlet Allocation (K = 8) was applied. Most of the top ranked topic words for the degree and betweenness centralities were consistent with the top 1% through the semantic network diagram. Among the important keywords examined every five years, "care" was unrivaled. When analyzing the two- and three-word combinations, there were many themes representing places, roles, and actions. As a result of performing topic modeling, eight topics were derived as ethical issues of decision-making for treatment withdrawal, symptom management to improve the quality of life, development of end-of-life knowledge education programs, life-sustaining care plan for elderly patients, home-based hospice, communication experience, patient symptom investigation, and an analysis of considering patient preferences. This study is meaningful as it analyzed a large amount of existing literature and considered the main trends of end-of-life care and nursing research based on the core subject control and semantic structure.

PMID: 33406715 [PubMed - in process]

Big Data Analytics + Virtual Clinical Semantic Network (vCSN): An Approach to Addressing the Increasing Clinical Nuances and Organ Involvement of COVID-19.

ASAIO J. 2021 01 01;67(1):18-24

Authors: Rahman F, Meyer R, Kriak J, Goldblatt S, Slepian MJ

Abstract
The coronavirus disease 2019 (COVID-19) pandemic has revealed deep gaps in our understanding of the clinical nuances of this extremely infectious viral pathogen. In order for public health, care delivery systems, clinicians, and other stakeholders to be better prepared for the next wave of SARS-CoV-2 infections, which, at this point, seems inevitable, we need to better understand this disease-not only from a clinical diagnosis and treatment perspective-but also from a forecasting, planning, and advanced preparedness point of view. To predict the onset and outcomes of a next wave, we first need to understand the pathologic mechanisms and features of COVID-19 from the point of view of the intricacies of clinical presentation, to the nuances of response to therapy. Here, we present a novel approach to model COVID-19, utilizing patient data from related diseases, combining clinical understanding with artificial intelligence modeling. Our process will serve as a methodology for analysis of the data being collected in the ASAIO database and other data sources worldwide.

PMID: 32796159 [PubMed - indexed for MEDLINE]

Utilization of text mining as a big data analysis tool for food science and nutrition.

Compr Rev Food Sci Food Saf. 2020 Mar;19(2):875-894

Authors: Tao D, Yang P, Feng H

Abstract
Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.

PMID: 33325182 [PubMed - in process]

Web-based interactive mapping from data dictionaries to ontologies, with an application to cancer registry.

BMC Med Inform Decis Mak. 2020 Dec 15;20(Suppl 10):271

Authors: Tao S, Zeng N, Hands I, Hurt-Mueller J, Durbin EB, Cui L, Zhang GQ

Abstract
BACKGROUND: The Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases from all healthcare facilities in the state within 6 months of diagnosis. Similar to all other U.S. and Canadian cancer registries, KCR uses a data dictionary provided by the North American Association of Central Cancer Registries (NAACCR) for standardized data entry. The NAACCR data dictionary is not an ontological system. Mapping between the NAACCR data dictionary and the National Cancer Institute (NCI) Thesaurus (NCIt) will facilitate the enrichment, dissemination and utilization of cancer registry data. We introduce a web-based system, called Interactive Mapping Interface (IMI), for creating mappings from data dictionaries to ontologies, in particular from NAACCR to NCIt.
METHOD: IMI has been designed as a general approach with three components: (1) ontology library; (2) mapping interface; and (3) recommendation engine. The ontology library provides a list of ontologies as targets for building mappings. The mapping interface consists of six modules: project management, mapping dashboard, access control, logs and comments, hierarchical visualization, and result review and export. The built-in recommendation engine automatically identifies a list of candidate concepts to facilitate the mapping process.
RESULTS: We report the architecture design and interface features of IMI. To validate our approach, we implemented an IMI prototype and pilot-tested features using the IMI interface to map a sample set of NAACCR data elements to NCIt concepts. 47 out of 301 NAACCR data elements have been mapped to NCIt concepts. Five branches of hierarchical tree have been identified from these mapped concepts for visual inspection.
CONCLUSIONS: IMI provides an interactive, web-based interface for building mappings from data dictionaries to ontologies. Although our pilot-testing scope is limited, our results demonstrate feasibility using IMI for semantic enrichment of cancer registry data by mapping NAACCR data elements to NCIt concepts.

PMID: 33319710 [PubMed - in process]

Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health.

BMC Med Inform Decis Mak. 2020 Dec 15;20(Suppl 10):269

Authors: Amith M, Fujimoto K, Mauldin R, Tao C

Abstract
BACKGROUND: Dyadic-based social networks analyses have been effective in a variety of behavioral- and health-related research areas. We introduce an ontology-driven approach towards social network analysis through encoding social data and inferring new information from the data.
METHODS: The Friend of a Friend (FOAF) ontology is a lightweight social network ontology. We enriched FOAF by deriving social interaction data and relationships from social data to extend its domain scope.
RESULTS: Our effort produced Friend of a Friend with Benefits (FOAF+) ontology that aims to support the spectrum of human interaction. A preliminary semiotic evaluation revealed a semantically rich and comprehensive knowledge base to represent complex social network relationships. With Semantic Web Rules Language, we demonstrated FOAF+ potential to infer social network ties between individual data.
CONCLUSION: Using logical rules, we defined interpersonal dyadic social connections, which can create inferred linked dyadic social representations of individuals, represent complex behavioral information, help machines interpret some of the concepts and relationships involving human interaction, query network data, and contribute methods for analytical and disease surveillance.

PMID: 33319708 [PubMed - in process]

Selected articles from the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019).

BMC Med Inform Decis Mak. 2020 Dec 14;20(Suppl 4):315

Authors: He Z, Tao C, Bian J, Zhang R

Abstract
In this introduction, we first summarize the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019) held on October 26, 2019 in conjunction with the 18th International Semantic Web Conference (ISWC 2019) in Auckland, New Zealand, and then briefly introduce seven research articles included in this supplement issue, covering the topics on Knowledge Graph, Ontology-Powered Analytics, and Deep Learning.

PMID: 33317524 [PubMed - in process]

Conversational ontology operator: patient-centric vaccine dialogue management engine for spoken conversational agents.

BMC Med Inform Decis Mak. 2020 Dec 14;20(Suppl 4):259

Authors: Amith M, Lin RZ, Cui L, Wang D, Zhu A, Xiong G, Xu H, Roberts K, Tao C

Abstract
BACKGROUND: Previously, we introduced our Patient Health Information Dialogue Ontology (PHIDO) that manages the dialogue and contextual information of the session between an agent and a health consumer. In this study, we take the next step and introduce the Conversational Ontology Operator (COO), the software engine harnessing PHIDO. We also developed a question-answering subsystem called Frankenstein Ontology Question-Answering for User-centric Systems (FOQUS) to support the dialogue interaction.
METHODS: We tested both the dialogue engine and the question-answering system using application-based competency questions and questions furnished from our previous Wizard of OZ simulation trials.
RESULTS: Our results revealed that the dialogue engine is able to perform the core tasks of communicating health information and conversational flow. Inter-rater agreement and accuracy scores among four reviewers indicated perceived, acceptable responses to the questions asked by participants from the simulation studies, yet the composition of the responses was deemed mediocre by our evaluators.
CONCLUSIONS: Overall, we present some preliminary evidence of a functioning ontology-based system to manage dialogue and consumer questions. Future plans for this work will involve deploying this system in a speech-enabled agent to assess its usage with potential health consumer users.

PMID: 33317519 [PubMed - in process]

A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets.

BMC Med Inform Decis Mak. 2020 Dec 14;20(Suppl 4):283

Authors: Zhang L, Hu J, Xu Q, Li F, Rao G, Tao C

Abstract
BACKGROUND: Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue.
METHODS: First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets.
RESULTS AND CONCLUSIONS: We focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson's disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.

PMID: 33317518 [PubMed - in process]

Antibiotic prescribing in UK care homes 2016-2017: retrospective cohort study of linked data.

BMC Health Serv Res. 2020 Jun 18;20(1):555

Authors: Smith CM, Williams H, Jhass A, Patel S, Crayton E, Lorencatto F, Michie S, Hayward AC, Shallcross LJ, Preserving Antibiotics through Safe Stewardship group

Abstract
BACKGROUND: Older people living in care homes are particularly susceptible to infections and antibiotics are therefore used frequently for this population. However, there is limited information on antibiotic prescribing in this setting. This study aimed to investigate the frequency, patterns and risk factors for antibiotic prescribing in a large chain of UK care homes.
METHODS: Retrospective cohort study of administrative data from a large chain of UK care homes (resident and care home-level) linked to individual-level pharmacy data. Residents aged 65 years or older between 1 January 2016 and 31 December 2017 were included. Antibiotics were classified by type and as new or repeated prescriptions. Rates of antibiotic prescribing were calculated and modelled using multilevel negative binomial regression.
RESULTS: 13,487 residents of 135 homes were included. The median age was 85; 63% residents were female. 28,689 antibiotic prescriptions were dispensed, the majority were penicillins (11,327, 39%), sulfonamides and trimethoprim (5818, 20%), or other antibacterials (4665, 16%). 8433 (30%) were repeat prescriptions. The crude rate of antibiotic prescriptions was 2.68 per resident year (95% confidence interval (CI) 2.64-2.71). Increased antibiotic prescribing was associated with residents requiring more medical assistance (adjusted incidence rate ratio for nursing opposed to residential care 1.21, 95% CI 1.13-1.30). Prescribing rates varied widely by care home but there were no significant associations with the care home-level characteristics available in routine data.
CONCLUSIONS: Rates of antibiotic prescribing in care homes are high and there is substantial variation between homes. Further research is needed to understand the drivers of this variation to enable development of effective stewardship approaches that target the influences of prescribing.

PMID: 32552886 [PubMed - indexed for MEDLINE]