Knowledge Networks of Biological and Medical Data: An Exhaustive and Flexible Solution to Model Life Science Domains

Losko, Sascha; Wenger, Karsten; Kalus, Wenzel; Ramge, Andrea; Wiehler, Jens; Heumann, Klaus G.

doi:10.1007/11799511_21

Cited by 7 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition two BioBridge specific datasets, 54 inflammation and tissue specific pathways and 122 COPD and exercise specific metabolite and enzyme concentrations and activities were manually curated from the literature within the project. The pathway curation followed a standard text-mining supported process as described for example in [ 43 ] while the enzyme concentration and activity curation was fully manual due to the small set of available relevant publications. To our knowledge BioBridge thus provides the first semantically integrated knowledge base of public COPD-specific information.…”

Section: Resultsmentioning

confidence: 99%

Knowledge management for Systems Biology a general and visually driven framework applied to translational medicine

et al. 2011

Self Cite

View full text Add to dashboard Cite

BackgroundTo enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory.ResultsTo address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data.ConclusionsWe generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

show abstract

Section: Resultsmentioning

confidence: 99%

Knowledge management for Systems Biology a general and visually driven framework applied to translational medicine

et al. 2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…The integrated text mining tool uses syntactic text parsing and dictionary-based named-entity recognition to extract semantically typed associations (such as ‘inhibits’, ‘activates’) between the defined semantic concepts (such as ‘gene’, ‘strain’) ( Losko et al, 2006 ). The initial task creates a defined text corpus, which includes uploaded relevant full text articles if applicable.…”

Section: Methodsmentioning

confidence: 99%

Informing epidemic (research) responses in a timely fashion by knowledge management - a Zika virus use case

Bauch

Pellet²,

Schleicher

et al. 2020

Biology Open

View full text Add to dashboard Cite

The response of pathophysiological research to emerging epidemics often occurs after the epidemic and, as a consequence, has little to no impact on improving patient outcomes or on developing high-quality evidence to inform clinical management strategies during the epidemic. Rapid and informed guidance of epidemic (research) responses to severe infectious disease outbreaks requires quick compilation and integration of existing pathophysiological knowledge. As a case study we chose the Zika virus (ZIKV) outbreak that started in 2015 to develop a proof-of-concept knowledge repository. To extract data from available sources and build a computationally tractable and comprehensive molecular interaction map we applied generic knowledge management software for literature mining, expert knowledge curation, data integration, reporting and visualisation. A multi-disciplinary team of experts, including clinicians, virologists, bioinformaticians and knowledge management specialists, followed a pre-defined workflow for rapid integration and evaluation of available evidence. While conventional approaches usually require months to comb through the existing literature, the initial ZIKV KnowledgeBase (ZIKA KB) was completed within a few weeks. Recently we updated the ZIKA KB with additional curated data from the large amount of literature published since 2016 and made it publicly available through a web interface together with a step-by-step guide to ensure reproducibility of the described use case. In addition, a detailed online user manual is provided to enable the ZIKV research community to generate hypotheses, share knowledge, identify knowledge gaps, and interactively explore and interpret data. A workflow for rapid response during outbreaks was generated, validated and refined and is also made available. The process described here can be used for timely structuring of pathophysiological knowledge for future threats. The resulting structured biological knowledge is a helpful tool for computational data analysis and generation of predictive models and opens new avenues for infectious disease research.

show abstract

“…The semantic networks of the knowledge model were set up in the BioXM TM Knowledge Management Environment version 6.1 by Biomax Informatics AG, Planegg, Germany. The BioXM platform mediates the database access for every semantic object (Losko et al, 2006; Losko and Heumann, 2009; Maier et al, 2011; Losko and Heumann, 2009). Every instance of a semantic object corresponds to a database entry.…”

Section: Methodsmentioning

confidence: 99%

From Matrices to Knowledge: Using Semantic Networks to Annotate the Connectome

Kopetzky

Butz‐Ostendorf

2018

Front. Neuroanat.

View full text Add to dashboard Cite

The connectome is regarded as the key to brain function in health and disease. Structural and functional neuroimaging enables us to measure brain connectivity in the living human brain. The field of connectomics describes the connectome as a mathematical graph with its connection strengths being represented by connectivity matrices. Graph theory algorithms are used to assess the integrity of the graph as a whole and to reveal brain network biomarkers for brain diseases; however, the faulty wiring of single connections or subnetworks as the structural correlate for neurological or mental diseases remains elusive. We describe a novel approach to represent the knowledge of human brain connectivity by a semantic network – a formalism frequently used in knowledge management to describe the semantic relations between objects. In our novel approach, objects are brain areas and connectivity is modeled as semantic relations among them. The semantic network turns the graph of the connectome into an explicit knowledge base about which brain areas are interconnected. Moreover, this approach can semantically enrich the measured connectivity of an individual subject by the semantic context from ontologies, brain atlases and molecular biological databases. Integrating all measurements and facts into one unified feature space enables cross-modal comparisons and analyses. We used a query mechanism for semantic networks to extract functional, structural and transcriptome networks. We found that in general higher structural and functional connectivity go along with a lower differential gene expression among connected brain areas; however, subcortical motor areas and limbic structures turned out to have a localized high differential gene expression while being strongly connected. In an additional explorative use case, we could show a localized high availability of fkbp5, gmeb1, and gmeb2 genes at a connection hub of temporo-limbic brain networks. Fkbp5 is known for having a role in stress-related psychiatric disorders, while gmeb1 and gmeb2 encode for modulator proteins of the glucocorticoid receptor, a key receptor in the hormonal stress system. Semantic networks tremendously ease working with multimodal neuroimaging and neurogenetics data and may reveal relevant coincidences between transcriptome and connectome networks.

show abstract

Knowledge Networks of Biological and Medical Data: An Exhaustive and Flexible Solution to Model Life Science Domains

Cited by 7 publications

References 6 publications

Knowledge management for Systems Biology a general and visually driven framework applied to translational medicine

Knowledge management for Systems Biology a general and visually driven framework applied to translational medicine

Informing epidemic (research) responses in a timely fashion by knowledge management - a Zika virus use case

From Matrices to Knowledge: Using Semantic Networks to Annotate the Connectome

Contact Info

Product

Resources

About