Semantic Data Integration and Knowledge Management to Represent Biological Network Associations.

The vast quantities of information generated by academic and industrial research groups are reflected in a rapidly growing body of scientific literature and exponentially expanding resources of formalized data, including experimental data, originating from a multitude of "-omics" platforms, phenotype information, and clinical data. For bioinformatics, the challenge remains to structure this information so that scientists can identify relevant information, to integrate this information as specific "knowledge bases," and to formalize this knowledge across multiple scientific domains to facilitate hypothesis generation and validation. Here we report on progress made in building a generic knowledge management environment capable of representing and mining both explicit and implicit knowledge and, thus, generating new knowledge. Risk management in drug discovery and clinical research is used as a typical example to illustrate this approach. In this chapter we introduce techniques and concepts (such as ontologies, semantic objects, typed relationships, contexts, graphs, and information layers) that are used to represent complex biomedical networks. The BioXM™ Knowledge Management Environment is used as an example to demonstrate how a domain such as oncology is represented and how this representation is utilized for research.

Toward an integrated knowledge environment to support modern oncology.

Around the world, teams of researchers continue to develop a wide range of systems to capture, store, and analyze data including treatment, patient outcomes, tumor registries, next-generation sequencing, single-nucleotide polymorphism, copy number, gene expression, drug chemistry, drug safety, and toxicity. Scientists mine, curate, and manually annotate growing mountains of data to produce high-quality databases, while clinical information is aggregated in distant systems. Databases are currently scattered, and relationships between variables coded in disparate datasets are frequently invisible. The challenge is to evolve oncology informatics from a "systems" orientation of standalone platforms and silos into an "integrated knowledge environments" that will connect "knowable" research data with patient clinical information. The aim of this article is to review progress toward an integrated knowledge environment to support modern oncology with a focus on supporting scientific discovery and improving cancer care.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine.

BACKGROUND: To 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. RESULTS: To 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. CONCLUSIONS: We 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.

Activation of epidermal growth factor receptor results in snail protein but not mRNA overexpression in endometrial cancer.

Reduced E-cadherin expression is associated with tumour progression of many carcinomas, including endometrial cancers. The transcription factor Snail is known as one of the most prominent transcriptional E-cadherin repressors; its regulation in cancer tissues, however, still remains unclear. Here, we report that activation of epidermal growth factor receptor (EGFR) resulted in overexpression of Snail and also identified critical downstream signalling molecules. Stimulation of two endometrial carcinoma cell lines with epidermal growth factor (EGF) lead to an increase of Snail protein expression. In primary human endometrioid endometrial carcinomas Snail protein expression correlated with the activated, phosphorylated form of EGFR (Tyr1086) as revealed by profiling 24 different signalling proteins using protein lysate microarrays. In addition, we observed an inverse correlation between Snail and E-cadherin protein levels in these tumours. Most likely, p38 MAPK, PAK1, AKT, ERK1/2 and GSK-3beta are involved in the up-regulation of Snail downstream of EGFR. Snail mRNA expression did not show a correlation with activated EGFR in these tumours. Taken together, profiling of signalling proteins in primary human tissues provided strong evidence that EGFR signalling is involved in Snail protein overexpression.

Semantic data integration and knowledge management to represent biological network associations.

The vast quantities of information generated by academic and industrial research groups are reflected in a rapidly growing body of scientific literature and exponentially expanding resources of formalized data including experimental data from "-omics" platforms, phenotype information, and clinical data. For bioinformatics, several challenges remain: to structure this information as biological networks enabling scientists to identify relevant information; to integrate this information as specific "knowledge bases"; and to formalize this knowledge across multiple scientific domains to facilitate hypothesis generation and validation and, thus, the generation of new knowledge. Risk management in drug discovery and clinical research is used as a typical example to illustrate this approach. In this chapter we will introduce techniques and concepts (such as ontologies, semantic objects, typed relationships, contexts, graphs, and information layers) that are used to represent complex biomedical networks. The BioXM Knowledge Management Environment is used as an example to demonstrate how a domain such as oncology is represented and how this representation is utilized for research.