Abstract
One of the most important types of posttranslational modification is phosphorylation which helps in the regulation of almost all activities of the cell. Phosphorylation is the process of addition of a phosphate group to a protein after the process of translation. In this paper, we have used evolutionary information extracted from position-specific scoring matrices (PSSM) to serve as features for prediction. Support vector machine (SVM) was used the machine learning tool. The system was tested with an independent set of 141 proteins for which our system achieved the highest AUC score of 0.7327. Additionally, our system attained best results for 34 proteins in terms of AUC.
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Authors are indebted to CMATER, department of computer science and Engineering, Jadavpur University for providing the necessary support for carrying out this experiment.
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Sagnik Banerjee, Debjyoti Ghosh, Subhadip Basu, Mita Nasipuri (2016). JUPred_SVM: Prediction of Phosphorylation Sites Using a Consensus of SVM Classifiers. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_45
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DOI: https://doi.org/10.1007/978-981-10-0448-3_45
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