Ontology Design Patterns for bio-ontologies: a case study on the Cell Cycle Ontology

<p>Abstract</p> <p>Background</p> <p>Bio-ontologies are key elements of knowledge management in bioinformatics. Rich and rigorous bio-ontologies should represent biological knowledge with high fidelity and robustness. The richness in bio-ontologies is a prior condition for diverse and efficient reasoning, and hence querying and hypothesis validation. Rigour allows a more consistent maintenance. Modelling such bio-ontologies is, however, a difficult task for bio-ontologists, because the necessary richness and rigour is difficult to achieve without extensive training.</p> <p>Results</p> <p>Analogous to design patterns in software engineering, Ontology Design Patterns are solutions to typical modelling problems that bio-ontologists can use when building bio-ontologies. They offer a means of creating rich and rigorous bio-ontologies with reduced effort. The concept of Ontology Design Patterns is described and documentation and application methodologies for Ontology Design Patterns are presented. Some real-world use cases of Ontology Design Patterns are provided and tested in the Cell Cycle Ontology. Ontology Design Patterns, including those tested in the Cell Cycle Ontology, can be explored in the Ontology Design Patterns public catalogue that has been created based on the documentation system presented (<url>http://odps.sourceforge.net/</url>).</p> <p>Conclusions</p> <p>Ontology Design Patterns provide a method for rich and rigorous modelling in bio-ontologies. They also offer advantages at different development levels (such as design, implementation and communication) enabling, if used, a more modular, well-founded and richer representation of the biological knowledge. This representation will produce a more efficient knowledge management in the long term.</p

e-Science and biological pathway semantics

<p>Abstract</p> <p>Background</p> <p>The development of e-Science presents a major set of opportunities and challenges for the future progress of biological and life scientific research. Major new tools are required and corresponding demands are placed on the high-throughput data generated and used in these processes. Nowhere is the demand greater than in the semantic integration of these data. Semantic Web tools and technologies afford the chance to achieve this semantic integration. Since pathway knowledge is central to much of the scientific research today it is a good test-bed for semantic integration. Within the context of biological pathways, the BioPAX initiative, part of a broader movement towards the standardization and integration of life science databases, forms a necessary prerequisite for its successful application of e-Science in health care and life science research. This paper examines whether BioPAX, an effort to overcome the barrier of disparate and heterogeneous pathway data sources, addresses the needs of e-Science.</p> <p>Results</p> <p>We demonstrate how BioPAX pathway data can be used to ask and answer some useful biological questions. We find that BioPAX comes close to meeting a broad range of e-Science needs, but certain semantic weaknesses mean that these goals are missed. We make a series of recommendations for re-modeling some aspects of BioPAX to better meet these needs.</p> <p>Conclusion</p> <p>Once these semantic weaknesses are addressed, it will be possible to integrate pathway information in a manner that would be useful in e-Science.</p

Clinical decision support using Open Data

First Online: 18 May 2020.The growth of Electronical Health Records (EHR) in healthcare has been gradual. However, a simple EHR system has become inefficient in supporting health professionals on decision making. In this sense, the need to acquire knowledge from storing data using open models and techniques has emerged, for the sake of improving the quality of service provided and to support the decision-making process. The usage of open models promotes interoperability between systems, communicating more efficiently. In this sense, the OpenEHR open data approach is applied, modelling data in two levels to distinguish knowledge from information. The application of clinical terminologies was fundamental in this study, in order to control data semantics based on coded clinical terms. This article culminated from the conceptualization of the knowledge acquisition process to represent Clinical Decision Support, using open data models.The work has been supported by FCT–Fundação para a Ciência e Tec-nologia within the Project Scope UID/CEC/00319/2019 and DSAIPA/DS/0084/2018

Obesity, diabetes and longevity in the Gulf: is there a Gulf Metabolic Syndrome?

The Gulf is experiencing a pandemic of lifestyle-induced obesity and type 2 diabetes mellitus (T2DM), with rates exceeding 50 and 30%, respectively. It is likely that T2DM represents the tip of a very large metabolic syndrome iceberg, which precedes T2DM by many years and is associated with abnormal/ectopic fat distribution, pathological systemic oxidative stress and inflammation. However, the definitions are still evolving with the role of different fat depots being critical. Hormetic stimuli, which include exercise, calorie restriction, temperature extremes, dehydration and even some dietary components (such as plant polyphenols), may well modulate fat deposition. All induce physiological levels of oxidative stress, which results in mitochondrial biogenesis and increased anti-oxidant capacity, improving metabolic flexibility and the ability to deal with lipids. We propose that the Gulf Metabolic Syndrome results from an unusually rapid loss of hormetic stimuli within an epigenetically important time frame of 2-3 generations. Epigenetics indicates that thriftiness can be programmed by the environment and passed down through several generations. Thus this loss of hormesis can result in continuation of metabolic inflexibility, with mothers exposing the foetus to a milieu that perpetuates a stressed epigenotype. As the metabolic syndrome increases oxidative stress and reduces life expectancy, a better descriptor may therefore be the Lifestyle-Induced Metabolic Inflexibility and accelerated AGEing syndrome – LIMIT-AGE. As life expectancy in the Gulf begins to fall, with perhaps a third of this life being unhealthy – including premature loss of sexual function, it is vital to detect evidence of this condition as early in life as possible. One effective way to do this is by detecting evidence of metabolic inflexibility by studying body fat content and distribution by magnetic resonance (MR). The Gulf Metabolic Syndrome thus represents an accelerated form of the metabolic syndrome induced by the unprecedented rapidity of lifestyle change in the region, the stress of which is being passed from generation to generation and may be accumulative. The fundamental cause is probably due to a rapid increase in countrywide wealth. This has benefited most socioeconomic groups, resulting in the development of an obesogenic environment as the result of the rapid adoption of Western labour saving and stress relieving devices (e.g. cars and air conditioning), as well as the associated high calorie diet

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

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results

Adherence with tobramycin inhaled solution and health care utilization

<p>Abstract</p> <p>Background</p> <p>Adherence with tobramycin inhalation solution (TIS) during routine cystic fibrosis (CF) care may differ from recommended guidelines and affect health care utilization.</p> <p>Methods</p> <p>We analyzed 2001-2006 healthcare claims data from 45 large employers. Study subjects had diagnoses of CF and at least 1 prescription for TIS. We measured adherence as the number of TIS therapy cycles completed during the year and categorized overall adherence as: low ≤ 2 cycles, medium >2 to <4 cycles, and high ≥ 4 cycles per year. Interquartile ranges (IQR) were created for health care utilization and logistic regression analysis of hospitalization risk was conducted by TIS adherence categories.</p> <p>Results</p> <p>Among 804 individuals identified with CF and a prescription for TIS, only 7% (n = 54) received ≥ 4 cycles of TIS per year. High adherence with TIS was associated with a decreased risk of hospitalization when compared to individuals receiving ≤ 2 cycles (adjusted odds ratio 0.40; 95% confidence interval 0.19-0.84). High adherence with TIS was also associated with lower outpatient service costs (IQR: $2,159-$8444 vs. $2,410-$14,423) and higher outpatient prescription drug costs (IQR: $35,125-$60,969 vs. $10,353-$46,768).</p> <p>Conclusions</p> <p>Use of TIS did not reflect recommended guidelines and may impact other health care utilization.</p