A5047229883- Biomedical Text Mining and Ontologies
- Semantic Web and Ontologies
- Scientific Computing and Data Management
- Electronic Health Records Systems
- Genomics and Rare Diseases
- Ethics in Clinical Research
- Bioinformatics and Genomic Networks
- Data Quality and Management
- Research Data Management Practices
- Computational Drug Discovery Methods
- Natural Language Processing Techniques
- Advanced Database Systems and Queries
- Radiomics and Machine Learning in Medical Imaging
- Metabolomics and Mass Spectrometry Studies
- Advanced Text Analysis Techniques
- AI in cancer detection
- linguistics and terminology studies
- Service-Oriented Architecture and Web Services
- Cellular Mechanics and Interactions
- 3D Printing in Biomedical Research
- Health, Environment, Cognitive Aging
- Multi-Agent Systems and Negotiation
- Gene expression and cancer classification
- Artificial Intelligence in Healthcare and Education
- Lung Cancer Diagnosis and Treatment
University of Arkansas for Medical Sciences
2015-2025
Institut Jules Bordet
2022
University of California, Los Angeles
2022
University of Florida Health
2019-2021
University of Regensburg
2021
University of Florida
2020
University of Pennsylvania
2018
Universidade Federal de Minas Gerais
2017
Saarland University
2007-2012
The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies provide a representation biomedical knowledge from Open Biological Ontologies (OBO) project adds ability this was derived. We here state several applications using it, such as adding semantic expressivity existing databases, building data entry forms,...
Although potential drug–drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete PDDI information. In the current study, all publically available sources information that could be identified using comprehensive and broad search were combined into dataset. The dataset merged fourteen different including 5 clinically-oriented sources, 4 Natural Language Processing (NLP) Corpora, Bioinformatics/Pharmacovigilance sources. As...
We built the Drug Ontology (DrOn) because we required correct and consistent drug information in a format for use semantic web applications, no existing resource met this requirement or could be altered to meet it. One of obstacles faced when creating DrOn was difficulty reusing from sources. The primary external source have used at stage DrOn’s development is RxNorm, standard terminology curated by National Library Medicine (NLM). To build DrOn, (1) mined data historical releases RxNorm (2)...
Reproducibility in untargeted metabolomics data processing remains a significant challenge due to software limitations and the complex series of steps required. To address these issues, we developed Nextflow4MS-DIAL, reproducible workflow for liquid chromatography-mass spectrometry (LC-MS) processing, validated with publicly available from MetaboLights (MTBLS733). Nextflow4MS-DIAL automates LC-MS minimize human errors manual handling. The supports containerization, ensuring computational...
This paper reports on original results of the Advancing Clinico-Genomic Trials Cancer integrated project focusing design and development a European biomedical grid infrastructure in support multicentric, postgenomic clinical trials (CTs) cancer. Postgenomic CTs use multilevel genomic data advanced computational analysis visualization tools to test hypothesis trying identify molecular reasons for disease stratification patients terms treatment. provides presentation needs users involved CTs,...
Summary Objectives: Biomedical ontologies exist to serve integration of clinical and experimental data, it is critical their success that they be put widespread use in the annotation data. How, then, can achieve sort user-friendliness, reliability, cost-effectiveness, breadth coverage necessary ensure extensive usage? Methods: Our focus here on two different sets answers these questions have been proposed, one hand medicine, by SNOMED CT community, other biology, OBO Foundry. We address more...
Biobanking necessitates extensive integration of data to allow analysis and specimen sharing. Ontologies have been demonstrated be a promising approach in fostering better semantic biobank-related data. Hitherto no ontology provided the coverage needed capture broad spectrum biobank user scenarios.Based principles laid out by Open Biological Biomedical Foundry two biobanking ontologies developed. These were merged using modular consistent with initial development principles. The merging was...
The Ontology of Medically Related Social Entities (OMRSE) was initially developed in 2011 to provide a framework for modeling demographic data Resource Description Framework/Web Language. It is built upon the Basic Formal and conforms Open Biomedical Ontologies Foundry's best practices. We report recent development OMRSE which includes representations organizations, roles, facilities, data, enrollment insurance plans, about socio-economic indicators. OMRSE's coverage has been expanding years...
Health and social care systems around the globe currently undergo a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering individual health status, conditions, genetic genomic dispositions, etc., in personal, social, occupational, environmental behavioral context. This is strongly supported by technologies such as micro- nanotechnologies, advanced computing, artificial intelligence, edge etc. For enabling communication cooperation...
Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology a controlled structured vocabulary consisting general terms (such as "cell" or "image" "tissue" "microscope") that form the basis such These are designed to represent types entities in domain reality has been devised capture; provided with logical definitions thereby also supporting reasoning over tagged data.This paper provides survey biomedical imaging ontologies have...
Biobanks are a critical resource for translational science. Recently, semantic web technologies such as ontologies have been found useful in retrieving research data from biobanks. However, recent has also shown that there is lack of about the administrative aspects These would be helpful to answer research-relevant questions what scope specimens collected biobank, curation status specimens, and contact information curators Our use cases include giving researchers ability retrieve key (e.g....
This paper provides an overview of current linguistic and ontological challenges which have to be met in order provide full support the transformation health ecosystems meet precision medicine (5 PM) standards. It highlights both standardization interoperability aspects regarding formal, controlled representations clinical research data, requirements for smart produce encode content a way that humans machines can understand process it. Starting from text-centered communication practices...
In this work, we describe the set of tools comprising Data Access Infrastructure within Advancing Clinic-genomic Trials on Cancer (ACGT), a R&D Project funded in part by European. This infrastructure aims at improving Post-genomic clinical trials providing seamless access to integrated clinical, genetic, and image databases. A data layer, based OGSA-DAI, has been developed order cope with syntactic heterogeneities The semantic problems present sources different nature are tackled two core...
Data management in post-genomic clinical trials is the process of collecting and validating genomic data with goal to answer research questions preserve it for future scientific investigation. Comprehensive metadata describing semantics are needed leverage further like cross-trial analysis. Current trial systems mostly lack sufficient not semantically interoperable. This paper outlines our approach develop an application that allows chairmen design their especially required system...
We developed the Apollo Structured Vocabulary (Apollo-SV)-an OWL2 ontology of phenomena in infectious disease epidemiology and population biology-as part a project whose goal is to increase use epidemic simulators public health practice. Apollo-SV defines terminology for simulator configuration. product an ontological analysis domain epidemiology, with particular attention inputs outputs nine simulators.Apollo-SV contains 802 classes representing simulators, which approximately half are new...