Ontology driven integration platform for clinical and translational research

Semantic Web technologies offer a promising framework for integration of disparate biomedical data. In this paper we present the semantic information integration platform under development at the Center for Clinical and Translational Sciences (CCTS) at the University of Texas Health Science Center at Houston (UTHSC-H) as part of our Clinical and Translational Science Award (CTSA) program. We utilize the Semantic Web technologies not only for integrating, repurposing and classification of multi-source clinical data, but also to construct a distributed environment for information sharing, and collaboration online. Service Oriented Architecture (SOA) is used to modularize and distribute reusable services in a dynamic and distributed environment. Components of the semantic solution and its overall architecture are described

Semantic Integration of Cervical Cancer Data Repositories to Facilitate Multicenter Association Studies: The ASSIST Approach

The current work addresses the unifi cation of Electronic Health Records related to cervical cancer into a single medical knowledge source, in the context of the EU-funded ASSIST research project. The project aims to facilitate the research for cervical precancer and cancer through a system that virtually unifi es multiple patient record repositories, physically located in different medical centers/hospitals, thus, increasing fl exibility by allowing the formation of study groups “on demand” and by recycling patient records in new studies. To this end, ASSIST uses semantic technologies to translate all medical entities (such as patient examination results, history, habits, genetic profi le) and represent them in a common form, encoded in the ASSIST Cervical Cancer Ontology. The current paper presents the knowledge elicitation approach followed, towards the defi nition and representation of the disease’s medical concepts and rules that constitute the basis for the ASSIST Cervical Cancer Ontology. The proposed approach constitutes a paradigm for semantic integration of heterogeneous clinical data that may be applicable to other biomedical application domains

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

ImmPort, toward repurposing of open access immunological assay data for translational and clinical research

Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components–Private Data, Shared Data, Data Analysis, and Resources—for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the ImmPort Shared Data portal , which allows research data to be repurposed to accelerate the translation of new insights into discoveries

OHMI: The Ontology of Host-Microbiome Interactions

Host-microbiome interactions (HMIs) are critical for the modulation of biological processes and are associated with several diseases, and extensive HMI studies have generated large amounts of data. We propose that the logical representation of the knowledge derived from these data and the standardized representation of experimental variables and processes can foster integration of data and reproducibility of experiments and thereby further HMI knowledge discovery. A community-based Ontology of Host-Microbiome Interactions (OHMI) was developed following the OBO Foundry principles. OHMI leverages established ontologies to create logically structured representations of microbiomes, microbial taxonomy, host species, host anatomical entities, and HMIs under different conditions and associated study protocols and types of data analysis and experimental results

The Infectious Disease Ontology in the Age of COVID-19

The Infectious Disease Ontology (IDO) is a suite of interoperable ontology modules that aims to provide coverage of all aspects of the infectious disease domain, including biomedical research, clinical care, and public health. IDO Core is designed to be a disease and pathogen neutral ontology, covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is then extended by a collection of ontology modules focusing on specific diseases and pathogens. In this paper we present applications of IDO Core within various areas of infectious disease research, together with an overview of all IDO extension ontologies and the methodology on the basis of which they are built. We also survey recent developments involving IDO, including the creation of IDO Virus; the Coronaviruses Infectious Disease Ontology (CIDO); and an extension of CIDO focused on COVID-19 (IDO-CovID-19).We also discuss how these ontologies might assist in information-driven efforts to deal with the ongoing COVID-19 pandemic, to accelerate data discovery in the early stages of future pandemics, and to promote reproducibility of infectious disease research

A network approach for managing and processing big cancer data in clouds

Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside

Background: Translational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery. Results: We developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action. Conclusions: This work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine. Availability: TMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql

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