Finding directionality and gene-disease predictions in disease associations
| Autor: | Manuel Garcia-Albornoz, Jens Nielsen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Systems biology
HUGO Gene Nomenclature Committee Disease Computational biology Biology Mitogen Activate Protein Kinase Pathway Structural Biology Modelling and Simulation Humans KEGG Gene Molecular Biology Genetic association KEGG Pathway Applied Mathematics Disease Network Computational Biology Phenotype KEGG Database Computer Science Applications Gene nomenclature Modeling and Simulation Metabolic Networks and Pathways Research Article |
| Zdroj: | BMC Systems Biology, 9 BMC Systems Biology |
| ISSN: | 1752-0509 |
| DOI: | 10.3929/ethz-b-000112316 |
| Popis: | Background Understanding the underlying molecular mechanisms in human diseases is important for diagnosis and treatment of complex conditions and has traditionally been done by establishing associations between disorder-genes and their associated diseases. This kind of network analysis usually includes only the interaction of molecular components and shared genes. The present study offers a network and association analysis under a bioinformatics frame involving the integration of HUGO Gene Nomenclature Committee approved gene symbols, KEGG metabolic pathways and ICD-10-CM codes for the analysis of human diseases based on the level of inclusion and hypergeometric enrichment between genes and metabolic pathways shared by the different human disorders. Methods The present study offers the integration of HGNC approved gene symbols, KEGG metabolic pathways andICD-10-CM codes for the analysis of associations based on the level of inclusion and hypergeometricenrichment between genes and metabolic pathways shared by different diseases. Results 880 unique ICD-10-CM codes were mapped to the 4315 OMIM phenotypes and 3083 genes with phenotype-causing mutation. From this, a total of 705 ICD-10-CM codes were linked to 1587 genes with phenotype-causing mutations and 801 KEGG pathways creating a tripartite network composed by 15,455 code-gene-pathway interactions. These associations were further used for an inclusion analysis between diseases along with gene-disease predictions based on a hypergeometric enrichment methodology. Conclusions The results demonstrate that even though a large number of genes and metabolic pathways are shared between diseases of the same categories, inclusion levels between these genes and pathways are directional and independent of the disease classification. However, the gene-disease-pathway associations can be used for prediction of new gene-disease interactions that will be useful in drug discovery and therapeutic applications. BMC Systems Biology, 9 ISSN:1752-0509 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink