Stop by and please say hello to some of our research neighbors in the Computational Biology Group on Linkedin. Each researcher's profile is linked within this article. These particular individuals responded to our request for those interested and working within the Semantic Web.
Professor of Computational Biology at U. Colorado Denver
My lab does research on knowledge-based tools for the analysis of genome-scale datasets. We use Semantic Web technology (OWL mostly) to do integration of database contents with text mining results. Some details are available on our web site (http://compbio.uchsc.edu/hunter), and others in some papers that are still under review.
I am a graduate student at the Penn State University, My advisor works on fungal genomics, and my project deals with the comparative genomics of certain select fungal genomes, i have been keenly interested in semantic web and its applications to inferring relationships on a genome scale. Ideally, i would like to build a semantic web application for deducing ortholog relationships between genes.
I am a software developer working at a Bioinformatics lab at the University of Georgia dealing with Apicomplexan Genomics. My Masters thesis work was based on Semantic Web and Semantic Web Services Technology. I have an interest in the medical field, and would like to hear more about the Medical Semantic Web and how I might contribute to that work.
Associate Professor at National Institute of Pharmaceutical Education and Research
I head a hybrid lab (both dry and wet). We use data mining, comparative genomics, homology modeling, docking (knowmics!), microarray, 2D gel electrophoresis (omics!) along with classical molecular biology (cloning, expression etc) to find drug target for therapeutic intervention against leishmania, fungus and oxidative stress.
I am coming from both worlds; I am an analytical biochemist involed in medical and analytical data integration and a computer scientist working on semantic integration of all these data towards a systems biology approach to understand underlying biological functions and to apply such knowledge to generate disease, toxicity or drug response profiles. These profiles or "singatures" then can be used to stratify patients, distinguish between responders and non-responders, reason about the biological responses and applying them to predict response or outcome in personalized medicine as well as toward better drug development.
We use scientific vocabularies to help manage and anlalyze data in several ways, but two functions are common to all our projects:
For collecting data: We use ontologies to improve quality of data annotation; species, cell types, experiment descriptions, etc. can all be constrained to use terminology from the appropriate ontology (for example, the NCBI taxonomy list). Of course, things change in research, so ontologies can, under certain circumstances, be customized.
For searching data: Since annotations on the web and in data repositories aren't always consistent or structured, we use ontologies and web search technology to make it easier, faster and more reliable to find relevant data. For example when searching for HER2 data, the system knows that ENTREZ gene defines "HER2" 'NEU" and "ERBB2" as synonyms, and that "human" and "homo sapiens" are synonyms and both are "primates".
