Knowledge Networks of Biological and Medical Data: An Exhaustive and Flexible Solution to Model Life Science Domains
- Cite this paper as:
- Losko S., Wenger K., Kalus W., Ramge A., Wiehler J., Heumann K. (2006) Knowledge Networks of Biological and Medical Data: An Exhaustive and Flexible Solution to Model Life Science Domains. In: Leser U., Naumann F., Eckman B. (eds) Data Integration in the Life Sciences. DILS 2006. Lecture Notes in Computer Science, vol 4075. Springer, Berlin, Heidelberg
The huge amount of unstructured information generated by academic and industrial research groups must be easily available to facilitate scientific projects. In particular, information that is conveyed by unstructured or semi-structured text represents a vast resource for the scientific community. Systems capable of mining these textual data sets are the only option to unveil the information hidden in free text on a large scale. The BioLT Literature Mining Tool allows exhaustive extraction of information from text resources. Using advanced tagger/parser mechanisms and topic-specific dictionaries, the BioLT tool delivers structured relationships. Beyond information hidden in free text, other resources in biological and medical research are relevant, including experimental data from “-omics” platforms, phenotype information and clinical data. The BioXM Knowledge Management Environment efficiently models such complex research environments. This platform enables scientists to create knowledge networks with flexible workflows for handling experimental information and metadata, including annotation or ontologies. Information from public databases can be incorporated using the embedded BioRS Integration and Retrieval System. Users can navigate and modify the information networks. Thus, research projects can be modeled and extended dynamically.
Unable to display preview. Download preview PDF.