Wikidata and the bibliography of life
Ontology highlight
ABSTRACT:
SUBMITTER: Page R
PROVIDER: S-EPMC9271275 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.
| S-EPMC7077981 | biostudies-literature
Project description:Publishing authoritative genomic annotation data, keeping it up to date, linking it to related information, and allowing community annotation is difficult and hard to support with limited resources. Here, we show how importing GeneDB annotation data into Wikidata allows for leveraging existing resources, integrating volunteer and scientific communities, and enriching the original information.
| S-EPMC6823904 | biostudies-literature
Project description:Wikidata is a free and open knowledge base which can be read and edited by both humans and machines. It acts as a central storage for the structured data of several Wikimedia projects. To improve the process of manually inserting new facts, the Wikidata platform features an association rule-based tool to recommend additional suitable properties. In this work, we introduce a novel approach to provide such recommendations based on frequentist inference. We introduce a trie-based method that can efficiently learn and represent property set probabilities in RDF graphs. We extend the method by adding type information to improve recommendation precision and introduce backoff strategies which further increase the performance of the initial approach for entities with rare property combinations. We investigate how the captured structure can be employed for property recommendation, analogously to the Wikidata PropertySuggester. We evaluate our approach on the full Wikidata dataset and compare its performance to the state-of-the-art Wikidata PropertySuggester, outperforming it in all evaluated metrics. Notably we could reduce the average rank of the first relevant recommendation by 71%.
| S-EPMC7250627 | biostudies-literature
Project description:The concept of Open Bibliography in science, technology and medicine (STM) is introduced as a combination of Open Source tools, Open specifications and Open bibliographic data. An Openly searchable and navigable network of bibliographic information and associated knowledge representations, a Bibliographic Knowledge Network, across all branches of Science, Technology and Medicine, has been designed and initiated. For this large scale endeavour, the engagement and cooperation of the multiple stakeholders in STM publishing - authors, librarians, publishers and administrators - is sought.
| S-EPMC3206455 | biostudies-literature
Project description:BACKGROUND: The statistical modeling of biomedical corpora could yield integrated, coarse-to-fine views of biological phenomena that complement discoveries made from analysis of molecular sequence and profiling data. Here, the potential of such modeling is demonstrated by examining the 5,225 free-text items in the Caenorhabditis Genetic Center (CGC) Bibliography using techniques from statistical information retrieval. Items in the CGC biomedical text corpus were modeled using the Latent Dirichlet Allocation (LDA) model. LDA is a hierarchical Bayesian model which represents a document as a random mixture over latent topics; each topic is characterized by a distribution over words. RESULTS: An LDA model estimated from CGC items had better predictive performance than two standard models (unigram and mixture of unigrams) trained using the same data. To illustrate the practical utility of LDA models of biomedical corpora, a trained CGC LDA model was used for a retrospective study of nematode genes known to be associated with life span modification. Corpus-, document-, and word-level LDA parameters were combined with terms from the Gene Ontology to enhance the explanatory value of the CGC LDA model, and to suggest additional candidates for age-related genes. A novel, pairwise document similarity measure based on the posterior distribution on the topic simplex was formulated and used to search the CGC database for "homologs" of a "query" document discussing the life span-modifying clk-2 gene. Inspection of these document homologs enabled and facilitated the production of hypotheses about the function and role of clk-2. CONCLUSION: Like other graphical models for genetic, genomic and other types of biological data, LDA provides a method for extracting unanticipated insights and generating predictions amenable to subsequent experimental validation.
| S-EPMC1533868 | biostudies-other
Project description: Not available
| S-EPMC9757093 | biostudies-literature
Project description:Open biological data are distributed over many resources making them challenging to integrate, to update and to disseminate quickly. Wikidata is a growing, open community database which can serve this purpose and also provides tight integration with Wikipedia. In order to improve the state of biological data, facilitate data management and dissemination, we imported all human and mouse genes, and all human and mouse proteins into Wikidata. In total, 59,721 human genes and 73,355 mouse genes have been imported from NCBI and 27,306 human proteins and 16,728 mouse proteins have been imported from the Swissprot subset of UniProt. As Wikidata is open and can be edited by anybody, our corpus of imported data serves as the starting point for integration of further data by scientists, the Wikidata community and citizen scientists alike. The first use case for these data is to populate Wikipedia Gene Wiki infoboxes directly from Wikidata with the data integrated above. This enables immediate updates of the Gene Wiki infoboxes as soon as the data in Wikidata are modified. Although Gene Wiki pages are currently only on the English language version of Wikipedia, the multilingual nature of Wikidata allows for usage of the data we imported in all 280 different language Wikipedias. Apart from the Gene Wiki infobox use case, a SPARQL endpoint and exporting functionality to several standard formats (e.g. JSON, XML) enable use of the data by scientists. In summary, we created a fully open and extensible data resource for human and mouse molecular biology and biochemistry data. This resource enriches all the Wikipedias with structured information and serves as a new linking hub for the biological semantic web. Database URL: https://www.wikidata.org/.
| S-EPMC4795929 | biostudies-literature
Project description:BackgroundPandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a "commons." Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases. However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modeled with entity schemas represented by Shape Expressions.ResultsAs a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable is demonstrated by integrating data from NCBI (National Center for Biotechnology Information) Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates.ConclusionsAlthough this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, human coronavirus NL63, human coronavirus 229E, human coronavirus HKU1, human coronavirus OC4).
| S-EPMC7820539 | biostudies-literature
Project description:BackgroundGiven the vast number of standards and formats for bibliographical data, any program working with bibliographies and citations has to be able to interpret such data. This paper describes the development of Citation.js (https://citation.js.org/), a tool to parse and format according to those standards. The program follows modern guidelines for software in general and JavaScript in specific, such as version control, source code analysis, integration testing and semantic versioning.ResultsThe result is an extensible tool that has already seen adaption in a variety of sources and use cases: as part of a server-side page generator of a publishing platform, as part of a local extensible document generator, and as part of an in-browser converter of extracted references. Use cases range from transforming a list of DOIs or Wikidata identifiers into a BibTeX file on the command line, to displaying RIS references on a webpage with added Altmetric badges to generating "How to cite this" sections on a blog. The accuracy of conversions is currently 27% for properties and 60% for types on average and a typical initialization takes 120 ms in browsers and 1 s with Node.js on the command line.ConclusionsCitation.js is a library supporting various formats of bibliographic information in a broad selection of use cases and environments. Given the support for plugins, more formats can be added with relative ease.
| S-EPMC7924481 | biostudies-literature
Project description:In 1997, lanbotulinumtoxinA (LAN) was introduced in China. It is now available in Asia, Latin America and Eastern Europe under various brand names including Hengli®, Lantox®, Prosigne®, Lanzox®, Redux®, Liftox®, HBTX-A and CBTX-A. The literature on LAN is mostly published in Chinese language, restricting its international accessibility. We, therefore, wanted to generate a complete English bibliography of all LAN publications and then use it for a comprehensive formalised literature review. Altogether, 379 LAN publications (322 in Chinese and 57 in English) were retrieved from PubMed and Science and Technology Paper Citation Database. Indications covered are motor (257), glandular (16), pain (32) and aesthetics (48). Topics are neurological (250), aesthetic (48), paediatric (38), ophthalmological (18), urological (9), methodological (6), gastroenterological (5), ear, nose and throat (4) and surgical (1). Seventy-one publications are randomised controlled trials, forty-one publications are interventional studies and observational studies, fifteen publications are case studies, eighteen publications are reviews, and two publications are guidelines. LAN publications cover all relevant topics of BT therapy throughout a period of more than 20 years. This constitutes a publication basis resembling those of other BT drugs. None of the LAN publications presents data contradictory to those generated with other BT type-A drugs. LAN seems to have a similar efficacy and safety features when compared to onabotulinumtoxinA using a 1:1 LAN- onabotulinumtoxinA conversion ratio. Large controlled multicentre studies will become necessary for LAN's registrations in Europe and North America.
| S-EPMC8224638 | biostudies-literature