Biomedical researchers working on a specific disease need up-to-date and unified access to knowledge relevant to the disease of their interest. Knowledge is continuously accumulated in scientific literature and other resourcessuch as biomedical ontologies. Identifying the specific information needed is a challenging task and computational tools can be valuable  . In this study, we propose a pipeline to automatically retrieve and integrate relevant knowledge based on a semantic graphrepresentation , the iASiS Open Data Graph. Results: The disease-specific semantic graph can provide easy access to resources relevant to specific concepts and individual aspects of these concepts , in the form of concept relations and attributes. The proposed approach is  applied to three different case studies: Two prevalent diseases , Lung Cancerand Dementia, for which a lot of knowledge is available, and one rare disease, Duchenne Muscular Dystrophy , for which knowledge is less abundant and difficult to locate. Results from exemplary queries are presented, investigating the potential of this approach in integrating and accessing knowledge as an automatically generated semantic graph  .
In biomedical research, unified access to up-to-date domain-specific knowledge is crucial, as such knowledge is continuously accumulated in scientific literature and structured resources. Identifying and extracting specific information is a challenging task and computational analysis of knowledge bases can be valuable in this direction. However, for disease-specific analyses researchers often need to compile their own datasets, integrating knowledge from different resources, or reuse existing datasets, that can be out-of-date . In this study, we propose a framework to automatically retrieve and integrate disease-specific knowledge into an up-to-date semantic graph , the iASiS Open Data Graph. This disease-specific semantic graph provides access to knowledge relevant to specific concepts and their individual aspects , in the form of concept relations and attributes. The proposed approach is implemented as an open-source framework and applied to three diseases (Lung Cancer, Dementia, and Duchenne Muscular Dystrophy ). Exemplary queries are presented, investigating the potential of this automatically generated semantic graph as a basis for retrieval and analysis of disease-specific knowledge .
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Biomedical researchers working on a specific disease need up-to-date and
In biomedical research,
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knowledge relevant to the disease of their interest. Knowledge is
up-to-date domain-specific knowledge is crucial, as such knowledge is
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other resourcessuch as biomedical ontologies. Identifying the specific information needed
structured resources. Identifying and extracting specific information
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tools
analysis of knowledge bases
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in this direction. However, for disease-specific analyses researchers often need to compile their own datasets, integrating knowledge from different resources, or reuse existing datasets, that can be out-of-date
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pipeline
framework
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relevant knowledge based on a semantic graphrepresentation
disease-specific knowledge into an up-to-date semantic graph
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Results: The
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can provide easy access to resources
provides access to knowledge
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individual aspects of these concepts
their individual aspects
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implemented as an open-source framework and
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different case studies: Two prevalent diseases , Lung Cancerand Dementia, for which a lot of knowledge is available, and one rare disease,
diseases (Lung Cancer, Dementia, and
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, for which knowledge is less abundant and difficult to locate. Results from exemplary
). Exemplary
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approach in integrating and accessing knowledge as an
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as a basis for retrieval and analysis of disease-specific knowledge