Abstract
It is widely recognized that the information for determining the final subcellular localization of proteins is found in their amino acid sequences. In this work we present new features extracted from the full length protein sequence to incorporate more biological information. Features are based on the occurrence frequency of di-peptides - traditional, higher order. Naïve Bayes classification along with correlation-based feature selection method is proposed to predict the subcellular location of apoptosis protein sequences. Our system makes predictions with an accuracy of 83% using Naïve Bayes classification alone and 86% using Naïve Bayes classification with correlation-based feature selection. This result shows that the new feature vector is promising, and helps in increasing the prediction accuracy.
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Govindan, G., Nair, A.S. (2011). New Feature Vector for Apoptosis Protein Subcellular Localization Prediction. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22709-7_30
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DOI: https://doi.org/10.1007/978-3-642-22709-7_30
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