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SVMRFE based approach for prediction of most discriminatory gene target for type II diabetes
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5331150/ https://www.ncbi.nlm.nih.gov/pubmed/28275550 http://dx.doi.org/10.1016/j.gdata.2017.02.008 |
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author | Kumar, Atul Jeya Sundara Sharmila, D. Singh, Sachidanand |
author_facet | Kumar, Atul Jeya Sundara Sharmila, D. Singh, Sachidanand |
author_sort | Kumar, Atul |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5331150 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-53311502017-03-08 SVMRFE based approach for prediction of most discriminatory gene target for type II diabetes Kumar, Atul Jeya Sundara Sharmila, D. Singh, Sachidanand Genom Data Regular Article 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. Elsevier 2017-02-17 /pmc/articles/PMC5331150/ /pubmed/28275550 http://dx.doi.org/10.1016/j.gdata.2017.02.008 Text en © 2017 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Regular Article Kumar, Atul Jeya Sundara Sharmila, D. Singh, Sachidanand SVMRFE based approach for prediction of most discriminatory gene target for type II diabetes |
title | SVMRFE based approach for prediction of most discriminatory gene target for type II diabetes |
title_full | SVMRFE based approach for prediction of most discriminatory gene target for type II diabetes |
title_fullStr | SVMRFE based approach for prediction of most discriminatory gene target for type II diabetes |
title_full_unstemmed | SVMRFE based approach for prediction of most discriminatory gene target for type II diabetes |
title_short | SVMRFE based approach for prediction of most discriminatory gene target for type II diabetes |
title_sort | svmrfe based approach for prediction of most discriminatory gene target for type ii diabetes |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5331150/ https://www.ncbi.nlm.nih.gov/pubmed/28275550 http://dx.doi.org/10.1016/j.gdata.2017.02.008 |
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