| Autor: |
Atul Kumar, D. Jeya Sundara Sharmila, Sachidanand Singh |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
|
| Zdroj: |
Genomics Data, Vol 12, Iss C, Pp 28-37 (2017) |
| Druh dokumentu: |
article |
| ISSN: |
2213-5960 |
| DOI: |
10.1016/j.gdata.2017.02.008 |
| Popis: |
Type II diabetes is a chronic condition that affects the way our body metabolizes sugar. The body's important source of fuel is now becoming a chronic disease all over the world. It is now very necessary to identify the new potential targets for the drugs which not only control the disease but also can treat it. Support vector machines are the classifier which has a potential to make a classification of the discriminatory genes and non-discriminatory genes. SVMRFE a modification of SVM ranks the genes based on their discriminatory power and eliminate the genes which are not involved in causing the disease. A gene regulatory network has been formed with the top ranked coding genes to identify their role in causing diabetes. To further validate the results pathway study was performed to identify the involvement of the coding genes in type II diabetes. The genes obtained from this study showed a significant involvement in causing the disease, which may be used as a potential drug target. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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