Pre-defined Ontologies Research Articles

Mulea: An R package for enrichment analysis using multiple ontologies and empirical false discovery rate

Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. mulea is distributed as a CRAN R package downloadable from https://cran.r-project.org/web/packages/mulea/ and https://github.com/ELTEbioinformatics/mulea. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Extraction, labelling, clustering, and semantic mapping of segments from clinical notes.

This work is motivated by the scarcity of tools for accurate, unsupervised information extraction from unstructured clinical notes in computationally underrepresented languages, such as Czech. We introduce a stepping stone to a broad array of downstream tasks such as summarisation or integration of individual patient records, extraction of structured information for national cancer registry reporting or building of semi-structured semantic patient representations that can be used for computing patient embeddings. More specifically, we present a method for unsupervised extraction of semantically-labelled textual segments from clinical notes and test it out on a dataset of Czech breast cancer patients, provided by Masaryk Memorial Cancer Institute (the largest Czech hospital specialising exclusively in oncology). Our goal was to extract, classify (i.e. label) and cluster segments of the free-text notes that correspond to specific clinical features (e.g., family background, comorbidities or toxicities). Finally, we propose a tool for computer-assisted semantic mapping of segment types to pre-defined ontologies and validate it on a downstream task of category-specific patient similarity. The presented results demonstrate the practical relevance of the proposed approach for building more sophisticated extraction and analytical pipelines deployed on Czech clinical notes.

Intelligent Integrative Platform for Sharing Heterogenuous Stem Cell Research Data.

Recent studies demonstrated that comparative analysis of stem cell research data sets originating from multiple studies can produce new information and help with hypotheses generation. Effective approaches for incorporating multiple diverse heterogeneous data sets collected from stem cell projects into a harmonized project-based framework have been lacking. Here, we provide an intelligent informatics solution for integrating comprehensive characterizations of stem cells with research subject and project outcome information. Our platform is the first to seamlessly integrate information from iPSCs and cancer stem cell research into a single platform, using a multi-modular common data element framework. Heterogeneous data is validated using predefined ontologies and stored in a relational database, to ensure data quality and ease of access. Testing was performed using 103 published, publicly-available iPSC and cancer stem cell projects conducted in clinical, preclinical and in vitro evaluations. We validated the robustness of the platform, by seamlessly harmonizing diverse data elements, and demonstrated its potential for knowledge generation through the aggregation and harmonization of data. Future aims of this project include increasing the database size using crowdsourcing and natural language processing functionalities. The platform is publicly available at https://remedy.mssm.edu/.

ReMeDy: a platform for integrating and sharing published stem cell research data with a focus on iPSC trials.

Recent regenerative medicine studies have emphasized the need for increased standardization, harmonization and sharing of information related to stem cell product characterization, to help drive these innovative interventions toward public availability and to increase collaboration in the scientific community. Although numerous attempts and numerous databases have been made to manage these data, a platform that incorporates all the heterogeneous data collected from stem cell projects into a harmonized project-based framework is still lacking. The aim of the database, which is described in this study, is to provide an intelligent informatics solution that integrates comprehensive characterization of diverse stem cell product characteristics with research subject and project outcome information. In the resulting platform, heterogeneous data are validated using predefined ontologies and stored in a relational database, to ensure data quality and ease of access. Testing was performed using 51 published, publically available induced pluripotent stem cell projects conducted in clinical, preclinical and in-vitro evaluations. Future aims of this project include further increasing the database size to include all published stem cell trials and develop additional data visualization tools to improve usability. Our testing demonstrated the robustness of the proposed platform, by seamlessly harmonizing diverse common data elements, and the potential of this platform for driving knowledge generation from the aggregation and harmonization of these diverse data.Database URL https://remedy.mssm.edu/

Introducing a Platform for Integrating and Sharing Stem Cell Research Data.

Advancements in regenerative medicine have highlighted the need for increased standardization and sharing of stem cell products to help drive these innovative interventions toward public availability and to increase collaboration in the scientific community. Although numerous attempts and numerous databases have been made to store this data, there is still a lack of a platform that incorporates heterogeneous stem cell information into a harmonized project-based framework. The aim of the platform described in this study, ReMeDy, is to provide an intelligent informatics solution which integrates diverse stem cell product characteristics with study subject and omics information. In the resulting platform, heterogeneous data is validated using predefined ontologies and stored in a relational database. In this initial feasibility study, testing of the ReMeDy functionality was performed using published, publically-available induced pluripotent stem cell projects conducted in in vitro, preclinical and intervention evaluations. It demonstrated the robustness of ReMeDy for storing diverse iPSC data, by seamlessly harmonizing diverse common data elements, and the potential utility of this platform for driving knowledge generation from the aggregation of this shared data. Next steps include increasing the number of curated projects by developing a crowdsourcing framework for data upload and an automated pipeline for metadata abstraction. The database is publically accessible at https://remedy.mssm.edu/.

Building a New Semantic Social Network Using Semantic Web-Based Techniques

Most people are more or less related to the web by participating in a kind of social networking site. Semantic Web technology plays a crucial role in these sites as they contain an enormous amount of data about ‎persons, pages, events, places, corporations, etc. This research is a Semantic Web application designed to create a new ‎semantic social community called Socialpedia. It links the already existing social public information to the newly ‎public ones. This information is linked with different information on the web to construct a new immense ‎data container. The resulting data container can be processed using a variety of Semantic Web techniques to produce ‎machine-understandable content. This content shows the promise of using integrated data to improve Web search and ‎Web-scale data analysis, unlike conventional search engines or social ones. This community involves obtaining data ‎from traditional users known as contributors or participants, linking data from existing social networks, extracting ‎structured data in triples using predefined ontologies, and finally querying and inferring such data to obtain ‎meaningful pieces of information. Socailpedia supports all popular functionalities of social networking websites ‎besides the enhanced features of the Semantic Web, providing advanced semantic search that acts as a semantic ‎search engine.

Towards Intelligent Integration and Sharing of Stem Cell Research Data.

Advancements in regenerative medicine have brought to the fore the need for increased standardization and sharing of stem cell product characterization to help drive these innovative interventions toward public availability. Although numerous attempts have been made to store this data, there is still a lack of a platform that incorporates heterogeneous stem cell information into a harmonized project-based framework. The aim of this project was to introduce and pilot-test an intelligent informatics solution which integrates diverse stem cell product characteristics with study subject and omics information. In the resulting platform, heterogeneous data is validated using predefined ontologies and stored in a NoSQL repository. Pilot-testing was performed on nine sponsored stem cell projects conducting preclinical and intervention evaluations. The pilot-testing demonstrated the robustness of the proposed platform, by seamlessly harmonizing diverse common data elements, and the potential of this platform for driving knowledge generation from the aggregation of this shared data.

What If Colorful Images Become More Important than Words? Visual Representations as the Basic Building Blocks of Human Communication and Dynamic Storytelling

Pre-historic communication included doing cave paintings, for example, of wild animals. Today, we continue to use images when communicating, most notably with videos and other such recordings. The advent and popularity of social networks on the Internet is based on the exchange of messages but for the most part on the sharing of images and videos. Although we are still generally very geared toward communication using words (and numbers) rather than visual representations, we foresee that a specific language that produces movement in the brain will soon be developed. The Business Narrative Modelling Language (BNML) has been used in various research contexts. BNML is based on pictorial representations, while incorporating business ontologies, which are studies of words and language to create a vocabulary that best represents relevant meanings in the business context to optimize information exchange. Do you prefer the narrative or do you prefer visual representations or would you rather have both? Providing examples of BNML, about servant leadership, we maintain that text without figures leads to incomplete communication, and vice versa—especially true for the “Millennium generation.” Leaders in business should strive to communicate more visually and diagrammatically, encouraging such communication among their employees, as much of knowledge today is tacit, and so difficult to communicate solely with the written word. Furthermore, words should be chosen carefully, to avoid overload and lack of comprehension, and using pre-defined ontologies is a good way of achieving this.

The tissue microarray OWL schema: An open-source tool for sharing tissue microarray data

Background: Tissue microarrays (TMAs) are enormously useful tools for translational research, but incompatibilities in database systems between various researchers and institutions prevent the efficient sharing of data that could help realize their full potential. Resource Description Framework (RDF) provides a flexible method to represent knowledge in triples, which take the form Subject- Predicate-Object. All data resources are described using Uniform Resource Identifiers (URIs), which are global in scope. We present an OWL (Web Ontology Language) schema that expands upon the TMA data exchange specification to address this issue and assist in data sharing and integration. Methods: A minimal OWL schema was designed containing only concepts specific to TMA experiments. More general data elements were incorporated from predefined ontologies such as the NCI thesaurus. URIs were assigned using the Linked Data format. Results: We present examples of files utilizing the schema and conversion of XML data (similar to the TMA DES) to OWL. Conclusion: By utilizing predefined ontologies and global unique identifiers, this OWL schema provides a solution to the limitations of XML, which represents concepts defined in a localized setting. This will help increase the utilization of tissue resources, facilitating collaborative translational research efforts.

Text-based over-representation analysis of microarray gene lists with annotation bias

A major challenge in microarray data analysis is the functional interpretation of gene lists. A common approach to address this is over-representation analysis (ORA), which uses the hypergeometric test (or its variants) to evaluate whether a particular functionally defined group of genes is represented more than expected by chance within a gene list. Existing applications of ORA have been largely limited to pre-defined terminologies such as GO and KEGG. We report our explorations of whether ORA can be applied to a wider mining of free-text. We found that a hitherto underappreciated feature of experimentally derived gene lists is that the constituents have substantially more annotation associated with them, as they have been researched upon for a longer period of time. This bias, a result of patterns of research activity within the biomedical community, is a major problem for classical hypergeometric test-based ORA approaches, which cannot account for such bias. We have therefore developed three approaches to overcome this bias, and demonstrate their usability in a wide range of published datasets covering different species. A comparison with existing tools that use GO terms suggests that mining PubMed abstracts can reveal additional biological insight that may not be possible by mining pre-defined ontologies alone.

OntoVote: a scalable distributed vote-collecting mechanism for ontology drift on a P2P platform

Ontologies provide potential support for knowledge and content management on a P2P platform. Although we can design ontologies beforehand for an application, it is argued that in P2P environments static or predefined ontologies cannot satisfy the ever-changing requirements of all users. So we propose every user should make proposals for what kind of ontology is the most apt to his need. Collecting all these proposals (or votes) helps the drift of ontologies. This paper introduces OntoVote, a scalable distributed vote-collecting mechanism based on application-level broadcast trees, and describes how OntoVote can be applied to ontology drift on a P2P platform by discussing several problems involved in the voting process.