Muntazir Mehdi

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The increase in the volume and heterogeneity of biomedical data sources has motivated researchers to embrace Linked Data (LD) technologies to solve the ensuing integration challenges and enhance information discovery. As an integral part of the EU GRANATUM project, a Linked Biomedical Dataspace (LBDS) was developed to semantically interlink data from(More)
Multiple datasets that add high value to biomedical research have been exposed on the web as a part of the Life Sciences Linked Open Data (LSLOD) Cloud. The ability to easily navigate through these datasets is crucial for personalized medicine and the improvement of drug discovery process. However, navigating these multiple datasets is not trivial as most(More)
The dynamicity of sensor data sources and publishing real-time sensor data over a generalised infrastructure like the Web pose a new set of integration challenges. Semantic Sensor Networks demand excessive expressivity for efficient formal analysis of sensor data. This article specifically addresses the problem of adapting data model specific or(More)
Preface It is our great pleasure to welcome you to the 7 th International Workshop on Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2014). SWAT4LS is an international workshop to meet and to present high-quality research results and relevant demos, applications and tutorials from all fields of Semantic Web technologies in Life Sciences. The(More)
The LOD cloud comprises of billions of facts covering hundreds of datasets. In accordance with the Linked Data principles, these datasets are connected by a variety of typed links, forming an interlinked "Web of Data". The growing diversity of the Web of Data makes it more and more challenging for publishers to find relevant datasets that could be linked(More)
A significant portion of the LOD cloud consists of Life Sciences data sets, which together contain billions of clinical facts that interlink to form a "Web of Clinical Data". However, tools for new publishers to find relevant datasets that could potentially be linked to are missing, particularly in specialist domain-specific settings. Based on a set of(More)
Biosimulation models have been recently introduced to understand the exact causative factors that give rise to impairment in human organs. Finite Element Method (FEM) provides a mathematical framework to simulate dynamic biological systems, with applications ranging from human ear, cardiovascular, to neurovascular research. Due to lack of a well-integrated(More)
BACKGROUND Several query federation engines have been proposed for accessing public Linked Open Data sources. However, in many domains, resources are sensitive and access to these resources is tightly controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS)(More)
Healthcare experts have recently turned towards the use of Biosimulation models to understand the multiple or different causative factors that cause impairment in human organs. The applications of biosimulations have been applied in different biological systems ranging from human ear, cardiovascular, to neurovascular research using Finite Element Method(More)