Abstract
One of the current issues in the bioinformatics domain is to identify genomic variations underlying the complex diseases. There are millions of genetic variations as well as environmental factors that may cause human diseases. Semantic web interlinks diverse data that may reveal many hidden relations and can be utilized for personalized medicine. This requires discovering relationships between phenotypes and genotypes, to answer how the genotype of an individual affects his/her health. Additionally, through identification of genomic variations based on an individual's genotype we can predict the response to a selected drug therapy and accordingly suggest treatment or drug regimes. A personalized medicine knowledgebase can interlink genotypic variations and its possible somatic changes that effects drug targets to pick best treatment and drug regimens for individuals. Such a knowledgebase may help to identify the factors that best explain the association between genotype and phenotype. We've used SPARQL queries to weight factors which link the genotype and phenotype via indirect relationships, and the paths of relationships. A personalized medicine knowledgebase build with the presented approach can interlink genotypic variations and its possible somatic changes that effects drug targets to pick best treatment and drug regimens for individuals, and may help to identify the factors that best explain the association between genotype and phenotype.
| Original language | English |
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| Title of host publication | International SWAT4LS Workshop - Semantic Web Application and Tools for life sciences |
| Publication status | Published - 2013 |
| Externally published | Yes |