Nacpred: Computational Prediction of Nac Proteins in Rice Implemented Using Smo Algorithm
| dc.contributor.author | Hemalatha, N. | |
| dc.contributor.author | Rajesh, M.K. | |
| dc.contributor.author | Narayanan, N.K. | |
| dc.date.accessioned | 2014-10-15T09:32:48Z | |
| dc.date.available | 2014-10-15T09:32:48Z | |
| dc.date.issued | 2013 | |
| dc.description.abstract | The impact of abiotic stresses, such as drought, on plant growth and development severely hampers crop production worldwide. The development of stress-tolerant crops will greatly benefit agricultural systems in areas prone to abiotic stresses. Recent advances in molecular and genomic technologies have resulted in a greater understanding of the mechanisms underlying the genetic control of the abiotic stress response in plants. NAC (NAM, ATAF1/2 and CUC2) domain proteins are plant-specific transcriptional factors which has diversified roles in various plant developmental processes and stress responses. More than 100 NAC genes have been identified in rice. In the proposed method, NACPred, an attempt has been made in the direction of computational prediction of NAC proteins. The well-known sequential minimum optimization (SMO) algorithm, which is most commonly used algorithm for numerical solutions of the support vector learning problems, has been used for the development of various modules in this tool. Modules were first developed using amino acid, traditional dipeptide (i+1), tripeptide (i+2) and an overall accuracy of 76%, 90%, and 97% respectively was achieved. To gain further insight, a hybrid module (hybrid1 and hybrid2) was also developed based on amino acid composition and dipeptide composition, which achieved an overall accuracy of 90% and 97%. To evaluate the prediction performance of NACPred, cross validation, leave one out validation and independent data test validation were carried out. It was also compared with algorithms namely RBF and Random Forest. The different statistical analyses worked out revealed that the proposed algorithm is useful for rice genome annotation, specifically predicting NAC proteins. | en_US |
| dc.identifier.citation | Advances in Computing, Communication, and Control, Third International Conference, ICAC3 2013 Mumbai, India, January 18-19, 2013 Proceedings | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/6013 | |
| dc.language.iso | en | en_US |
| dc.subject | Rice | en_US |
| dc.subject | NAC | en_US |
| dc.subject | gene prediction | en_US |
| dc.subject | SMO | en_US |
| dc.title | Nacpred: Computational Prediction of Nac Proteins in Rice Implemented Using Smo Algorithm | en_US |
| dc.type | Article | en_US |