1,527 research outputs found
Coordinating virus research: The Virus Infectious Disease Ontology
The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies––structured, controlled, vocabularies––are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects
The DO-KB Knowledgebase: a 20-year journey developing the disease open science ecosystem.
In 2003, the Human Disease Ontology (DO, https://disease-ontology.org/) was established at Northwestern University. In the intervening 20 years, the DO has expanded to become a highly-utilized disease knowledge resource. Serving as the nomenclature and classification standard for human diseases, the DO provides a stable, etiology-based structure integrating mechanistic drivers of human disease. Over the past two decades the DO has grown from a collection of clinical vocabularies, into an expertly curated semantic resource of over 11300 common and rare diseases linking disease concepts through more than 37000 vocabulary cross mappings (v2023-08-08). Here, we introduce the recently launched DO Knowledgebase (DO-KB), which expands the DO\u27s representation of the diseaseome and enhances the findability, accessibility, interoperability and reusability (FAIR) of disease data through a new SPARQL service and new Faceted Search Interface. The DO-KB is an integrated data system, built upon the DO\u27s semantic disease knowledge backbone, with resources that expose and connect the DO\u27s semantic knowledge with disease-related data across Open Linked Data resources. This update includes descriptions of efforts to assess the DO\u27s global impact and improvements to data quality and content, with emphasis on changes in the last two years
The Epidemiology and Management of Kawasaki Disease in Australia
Kawasaki disease (KD) is a syndrome of systemic inflammation with the potential to cause life-threatening aneurysms of the coronary arteries. I sought to contribute to our understanding of this important condition, particularly with regard to Australian children.
By determining the hospitalisation rate and IVIG-treatment rate I estimated the incidence of KD to be about 14 per 100,000 children under the age of 5 between 2007 and 2015. I also showed that the hospitalisation rate nationally had increased on average 3.5% annually between 1993 and 2018, with significant changes in the age distribution over that period.
In collaboration with the Paediatric Active Enhanced Disease Surveillance (PAEDS) network, I undertook a large multicentre prospective surveillance study of KD in Australia. My analysis of that cohort confirmed several of the findings from the survey, such as the preference of Australian clinicians for low-dose aspirin from the time of diagnosis, and the considerable variability around how IVIG resistance is diagnosed and managed. Importantly, I observed that a significant subset of children diagnosed with, and treated for, KD do not meet the diagnostic criteria outlined in the 2017 statement by the American Heart Association.
This work has contributed significantly to the understanding of KD’s epidemiology, management, and outcomes in Australia. I have shown that the incidence of the condition is increasing, and the clinical picture is changing. I identified important areas of practice variation and highlighted the need for international collaboration around agreed definitions (such as for IVIG resistance). Finally, I have played a central role in establishing an important resource for future resource: prospective surveillance of KD in Australia continues, with well over 700 cases recruited so far. It is hoped that this work will be of benefit to the researchers, clinicians, patients, and families affected by KD now, and into the future
30th European Congress on Obesity (ECO 2023)
This is the abstract book of 30th European Congress on Obesity (ECO 2023
CIViCdb 2022: Evolution of an open-access cancer variant interpretation knowledgebase
CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC\u27s functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing \u3e3200 variants in \u3e470 genes from \u3e3100 publications
Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability
The integration of Artificial Intelligence (AI) into the field of drug
discovery has been a growing area of interdisciplinary scientific research.
However, conventional AI models are heavily limited in handling complex
biomedical structures (such as 2D or 3D protein and molecule structures) and
providing interpretations for outputs, which hinders their practical
application. As of late, Graph Machine Learning (GML) has gained considerable
attention for its exceptional ability to model graph-structured biomedical data
and investigate their properties and functional relationships. Despite
extensive efforts, GML methods still suffer from several deficiencies, such as
the limited ability to handle supervision sparsity and provide interpretability
in learning and inference processes, and their ineffectiveness in utilising
relevant domain knowledge. In response, recent studies have proposed
integrating external biomedical knowledge into the GML pipeline to realise more
precise and interpretable drug discovery with limited training instances.
However, a systematic definition for this burgeoning research direction is yet
to be established. This survey presents a comprehensive overview of
long-standing drug discovery principles, provides the foundational concepts and
cutting-edge techniques for graph-structured data and knowledge databases, and
formally summarises Knowledge-augmented Graph Machine Learning (KaGML) for drug
discovery. A thorough review of related KaGML works, collected following a
carefully designed search methodology, are organised into four categories
following a novel-defined taxonomy. To facilitate research in this promptly
emerging field, we also share collected practical resources that are valuable
for intelligent drug discovery and provide an in-depth discussion of the
potential avenues for future advancements
Entity Linking in Low-Annotation Data Settings
Recent advances in natural language processing have focused on applying and adapting large pretrained language models to specific tasks. These models, such as BERT (Devlin et al., 2019) and BART (Lewis et al., 2020a), are pretrained on massive amounts of unlabeled text across a variety of domains. The impact of these pretrained models is visible in the task of entity linking, where a mention of an entity in unstructured text is matched to the relevant entry in a knowledge base. State-of-the-art linkers, such as Wu et al. (2020) and De Cao et al. (2021), leverage pretrained models as a foundation for their systems. However, these models are also trained on large amounts of annotated data, which is crucial to their performance. Often these large datasets consist of domains that are easily annotated, such as Wikipedia or newswire text. However, tailoring NLP tools to a narrow variety of textual domains severely restricts their use in the real world.
Many other domains, such as medicine or law, do not have large amounts of entity linking annotations available. Entity linking, which serves to bridge the gap between massive unstructured amounts of text and structured repositories of knowledge, is equally crucial in these domains. Yet tools trained on newswire or Wikipedia annotations are unlikely to be well-suited for identifying medical conditions mentioned in clinical notes. As most annotation efforts focus on English, similar challenges can be noted in building systems for non-English text. There is often a relatively small amount of annotated data in these domains. With this being the case, looking to other types of domain-specific data, such as unannotated text or highly-curated structured knowledge bases, is often required. In these settings, it is crucial to translate lessons taken from tools tailored for high-annotation domains into algorithms that are suited for low-annotation domains. This requires both leveraging broader types of data and understanding the unique challenges present in each domain
Metaverse. Old urban issues in new virtual cities
Recent years have seen the arise of some early attempts to build virtual cities,
utopias or affective dystopias in an embodied Internet, which in some respects appear to
be the ultimate expression of the neoliberal city paradigma (even if virtual). Although
there is an extensive disciplinary literature on the relationship between planning and
virtual or augmented reality linked mainly to the gaming industry, this often avoids design
and value issues. The observation of some of these early experiences - Decentraland,
Minecraft, Liberland Metaverse, to name a few - poses important questions and problems
that are gradually becoming inescapable for designers and urban planners, and allows
us to make some partial considerations on the risks and potentialities of these early virtual
cities
Development of topical ophthalmic formulations for fungal keratitis treatment
In the present thesis, different topical-ophthalmic formulations of antifungals
and cyclodextrins were developed for the treatment of fungal keratitis. In addition, the safety and permanence on
the ocular surface of different cyclodextrins were evaluated in order to know their behavior in the eye and to
optimize their use in the development of new ophthalmic formulations
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