NACSVMPred: A Machine Learning Approach for Prediction of NAC Proteins in Rice Using Support Vector Machines
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Date
2012
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Abstract
NAC proteins are plant-specific transcriptional factors with diversified roles in various developmental processes and stress responses. Development of genome wide prediction tools for NAC proteins will substantially have an impact on rice gene annotation. NACSVMPred is an effort in this direction for computational genome-scale prediction of NAC proteins in rice by integrating compositional and evolutionary information of proteins. Support vector machine (SVM)-based modules were first developed using traditional amino acid, dipeptide (i+1), tripeptide (i+2), four-parts composition and PSSM and an overall accuracy of 79%, 93%, 93%, 79% and 100% respectively was achieved. Further, two hybrid modules were developed based on amino acid, dipeptide and tripeptide composition, which achieved an overall accuracy of 83% and 79%. NACSVMPred was also evaluated with PSI-BLAST, which resulted in a lower accuracy of 50%. The different statistical analyses carried out revealed that the proposed algorithm is useful for rice genome annotation, specifically predicting NAC proteins.
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SVM, NAC, RBF, PSSM
Citation
In: Proceedings of the first International Conference on Advanced Information Technologies & Applications 2012 (ICAITA 2012), held on December 28th - 29th 2012, Dubai, UAE, Computer Science & Information Technology (CS & IT)(eds. Sundarapandian, V. and Nagamalai, D.). ICAITA, SAI, SEAS,CDKP, CMCA, CS & IT 08, pp.83-95