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- 21 title "Adaptive Linked Data-driven Web Components: Building Flexible and Reusable Semantic Web Interfaces".
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- item-1 hasContent ali-khalili.
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- item-2 hasContent antonis-loizou.
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- item-3 hasContent frank-van-harmelen.
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- 234 creator achille-fokoue.
- 234 creator mohammad-sadoghi.
- 234 creator oktie-hassanzadeh.
- 234 creator ping-zhang.
- 234 type InProceedings.
- 234 label "Predicting Drug-Drug Interactions through Large-Scale Similarity-Based Link Prediction".
- 234 sameAs 234.
- 234 abstract "Drug-Drug Interactions (DDIs) are a major cause of preventable adverse drug reactions (ADRs), causing a significant burden on the patients' health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. We present Tiresias, a framework that takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed approach and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs and among newly developed and existing drugs.".
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- 234 title "Predicting Drug-Drug Interactions through Large-Scale Similarity-Based Link Prediction".
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- item-1 hasContent achille-fokoue.
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- item-2 hasContent mohammad-sadoghi.
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- item-3 hasContent oktie-hassanzadeh.
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- 37 creator bojan-bozic.
- 37 creator gavin-mendel-gleason.
- 37 creator harvey-whitehouse.
- 37 creator kevin-feeney.
- 37 creator peter-turchin.
- 37 creator pieter-francois.
- 37 creator rob-brennan.
- 37 creator stephanie-grohmann.
- 37 creator thomas-e-currie.
- 37 type InProceedings.
- 37 label "Building the Seshat Ontology for a Global History Databank".
- 37 sameAs 37.
- 37 abstract "This paper describes the development of an OWL DL ontology from the so-cial science codebook (schema) developed by the Seshat: Global History Databank. The ontology describes human history as a set of over 1500 time series variables and supports expression of uncertainty, temporal scoping, annotations and bibliographic references. The Seshat ontology was developed to transition from traditional social science data collection and storage tech-niques to an RDF-based semantic representation in order to support auto-mated generation of data entry and validation tools, data quality manage-ment processes, rich interlinking with the web of data, and management of the data curation lifecycle. This ontology engineering and codebook translation exercise identified several pitfalls in modelling social science codebooks with semantic web technologies; provided insights into the practical application of OWL DL to complex, real-world modelling challenges; and has enabled the construction of new, RDF-based tools to support the large-scale data curation effort in Seshat. The Seshat ontology exhibits a set of ontology design patterns for model-ling annotated and bibliographically referenced data, uncertainty or temporal bounds in standard RDF triplestores without requiring custom reasoning or storage engines. These patterns and the lessons learned from translating the Seshat codebook into RDF provide some generally useful guidance for the development and deployment of RDF models in the social sciences. Our ontology has been developed to support the large scale data collection and curation effort involved in building the Seshat: Global History Data-bank. OWL-based quality management will assure the data is suitable for statistical analysis. Publication of Seshat as high-quality, linked open data will enable other researchers to build on it.".
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- 37 title "Building the Seshat Ontology for a Global History Databank".
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