Architecture and usability of OntoKeeper, an ontology evaluation tool

Abstract Background The existing community-wide bodies of biomedical ontologies are known to contain quality and content problems. Past research has revealed various errors related to their semantics and logical structure. Automated tools may help to ease the ontology construction, maintenance, assessment and quality assurance processes. However, there are relatively few tools that exist that can provide this support to knowledge engineers. Method We introduce OntoKeeper as a web-based tool that can automate quality scoring for ontology developers. We enlisted 5 experienced ontologists to test the tool and then administered the System Usability Scale to measure their assessment. Results In this paper, we present usability results from 5 ontologists revealing high system usability of OntoKeeper, and use-cases that demonstrate its capabilities in previous published biomedical ontology research. Conclusion To the best of our knowledge, OntoKeeper is the first of a few ontology evaluation tools that can help provide ontology evaluation functionality for knowledge engineers with good usability.https://deepblue.lib.umich.edu/bitstream/2027.42/152214/1/12911_2019_Article_859.pd

Metrics and methods for comparative ontology evaluation

While progress has been made toward describing the need for ontology evaluation and offering proposals concerning what properties to measure and how, work remains to develop ontology evaluation as a rigorous discipline. Ontologies as information artifacts have a variety of aspects that can inform their evaluation, both in terms of what is evaluated and the metrics used. Ontology evaluation as a discipline requires (1) having a systematic account of the different aspects of ontologies and the properties relevant to those aspects, (2) critically developing methods for examining those properties, (3) developing comparative metrics that allow ontology engineers to compare the effects of various modeling choices and allow users to compare the merits of existing ontologies, and (4) charting possible pitfalls of evaluation. This paper considers various properties of ontologies that have been proposed and organizes these properties according to different aspects of ontologies. To begin bringing previous work together and to illustrate where pitfalls and potential solutions might enter into a rigorous evaluation, I offer a more in depth (though still partial) analysis of evaluating the correctness of ontologies. I conclude with a discussion of next steps in systematizing ontology evaluation

Design and Architecture of an Ontology-driven Dialogue System for HPV Vaccine Counseling

Speech and conversational technologies are increasingly being used by consumers, with the inevitability that one day they will be integrated in health care. Where this technology could be of service is in patient-provider communication, specifically for communicating the risks and benefits of vaccines. Human papillomavirus (HPV) vaccine, in particular, is a vaccine that inoculates individuals from certain HPV viruses responsible for adulthood cancers - cervical, head and neck cancers, etc. My research focuses on the architecture and development of speech-enabled conversational agent that relies on series of consumer-centric health ontologies and the technology that utilizes these ontologies. Ontologies are computable artifacts that encode and structure domain knowledge that can be utilized by machines to provide high level capabilities, such as reasoning and sharing information. I will focus the agent’s impact on the HPV vaccine domain to observe if users would respond favorably towards conversational agents and the possible impact of the agent on their beliefs of the HPV vaccine. The approach of this study involves a multi-tier structure. The first tier is the domain knowledge base, the second is the application interaction design tier, and the third is the feasibility assessment of the participants. The research in this study proposes the following questions: Can ontologies support the system architecture for a spoken conversational agent for HPV vaccine counseling? How would prospective users’ perception towards an agent and towards the HPV vaccine be impacted after using conversational agent for HPV vaccine education? The outcome of this study is a comprehensive assessment of a system architecture of a conversational agent for patient-centric HPV vaccine counseling. Each layer of the agent architecture is regulated through domain and application ontologies, and supported by the various ontology-driven software components that I developed to compose the agent architecture. Also discussed in this work, I present preliminary evidence of high usability of the agent and improvement of the users’ health beliefs toward the HPV vaccine. All in all, I introduce a comprehensive and feasible model for the design and development of an open-sourced, ontology-driven conversational agent for any health consumer domain, and corroborate the viability of a conversational agent as a health intervention tool

A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

NEural models for ontology annotations - NEMO

The rapid progression of technology has allowed a significant increase in the pace of modern, novel scientific experimentations. Important results from these experiments are often buried in rather comprehensive documents and thus information retrieval is difficult. To facilitate retrieval and knowledge discovery, domain experts have been using ontologies (a formal way to represent knowledge within a domain) to annotate important entities. These annotations are generally curated manually which is a slow and laborious process and hence unscalable. As a solution for scalable ontology annotations, Named Entity Recognition (NER) is critical. NER is the task of recognizing ontology concepts from the text. Traditionally, entity recognition was achieved using syntactic analysis, lexical approaches, and traditional machine learning. In recent years, deep learning has shown improved results in terms of concept recognition. This research explores different approaches to improve the state-of-the-art deep learning models for automated ontology annotations. Here, CRAFT (a manually curated biomedical corpus for ontologies) is used as a gold standard corpus for training and evaluating the performance of different deep learning architectures. We augment the information from CRAFT with several existing knowledge bases. This study demonstrates that we can improve the prediction accuracy of existing deep learning models by including additional information as input pipelines to existing architectures. Additionally, ontologies are hierarchical and have semantic relations between concepts. While deep learning models generally fail to take this hierarchy into account, our work also explores the possibility of making the models ontology-aware and shows improvement over baseline models. Furthermore, we implement a novel concept called Ontology Boosting to boost the prediction accuracy of pre-trained models through post-processing steps

Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations

Personalized medicine is a concept that has been subject of increasing interest in medical research and practice in the last few years. However, significant challenges stand in the way of practical implementations, namely in regard to extracting clinically valuable insights from the vast amount of biomedical knowledge generated in the last few years. Here, we describe an approach that uses Knowledge Graph Embedding (KGE) methods on a biomedical Knowledge Graph (KG) as a path to reasoning over the wealth of information stored in publicly accessible databases. We built a Knowledge Graph using data from DisGeNET and GO, containing relationships between genes, diseases and other biological entities. The KG contains 93,657 nodes of 5 types and 1,705,585 relationships of 59 types. We applied KGE methods to this KG, obtaining an excellent performance in predicting gene-disease associations (MR 0.13, MRR 0.96, HITS@1 0.93, HITS@3 0.99, and HITS@10 0.99). The optimal hyperparameter set was used to predict all possible novel gene-disease associations. An in-depth analysis of novel gene-disease predictions for disease terms related to Autism Spectrum Disorder (ASD) shows that this approach produces predictions consistent with known candidate genes and biological pathways and yields relevant insights into the biology of this paradigmatic complex disorder.Fundação para a Ciência e a Tecnologia, Grant/Award Numbers: SAICTPAC/0010/2015, POCI- 01-0145-FEDER-016428-PAC, EXPL/CCI-BIO/0126/2021, PTDC/MED-OUT/28937/2017, UIDP/04046/2020, UIDB/04046/2020; Fundo Europeu de Desenvolvimento Regional, Grant/Award Number: 022153info:eu-repo/semantics/publishedVersio

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

Transcriptomics of the human airway epithelium reflect the physiologic response to inhaled environmental pollutants

Current methods for the risk assessment of environmental exposures commonly involve questionnaires, stationary monitoring, and personal air sampling. However, as these approaches do not capture the body's internal response, they lend minimal understanding to the biologic consequence of exposure. In order to address the unmet need of connecting external exposure measurements with signatures of internal exposure, this thesis examines the overarching hypothesis that transcriptomic changes in the human airway epithelium can serve as indicators of physiologic responses to inhaled pollutants. This is an extension of previous work that has demonstrated an airway ''field of injury'' effect where cigarette smoke exposure alters gene-expression in epithelial cells lining the respiratory tract. Specifically, I examine transcriptomic changes and the biologic responses associated with exposure to the following pollutants: environmental tobacco smoke (Aim 1), household air pollution from smoky coal combustion (Aim 2), and electronic cigarette vapor (Aim 3). First, I performed whole-genome transcriptional profiling of the nasal epithelium in children and adults and detected gene-expression changes associated with exposure to environmental tobacco smoke. Next, I employed similar approaches to detect a signature of coal smoke exposure in the buccal epithelium of healthy, non-smoking females exposed to household air pollution Xuanwei, China. The findings from these studies suggest that upper airway gene-expression can reflect the host response to prolific sources of environmental exposures that are major risk factors for chronic lung disease. Lastly, I examine the cellular and physiologic consequences of electronic cigarette (ECIG) aerosol exposure by analyzing transcriptomic profiles of human bronchial epithelial cells that have either been (1) differentiated and exposed in vitro or (2) acquired via bronchoscopy from the airway epithelium of ECIG users. The studies detailed in this dissertation offer valuable insight that will accelerate the efforts to evaluate the health effects of both well-established and emerging types of inhaled exposures in large-scale population studies. Furthermore, the transcriptomic strategies woven throughout the following chapters push for a novel assessment paradigm that may enable the public health community to rapidly characterize the physiologic host response to inhalation exposures of different sources, and to evaluate the biologic consequences of exposure-reduction initiatives.2017-05-01T00:00:00