Abstract
This paper presents a supervised Back Propagation Neural network (BPN) for the hub characterization of the tumor protein P53. This paper proposes a method to predict the P53 protein as Hub or Non-Hub from the sequence information of protein alone. The hubness characterization of this protein has been carried out using Hydrophobicity, one of the important physio-chemical properties of the amino acid. The proposed method has been tested on the P53 and its interacting proteins successfully. The same method on the whole set of Human proteins from the database HPRD and APID has shown around 90% of accuracy, sensitivity and specificity with the help of Artificial Neural Network (ANN).
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Sajeev, J., Mahalakshmi, T. (2011). Hub Characterization of Tumor Protein P53 Using Artificial Neural Networks. 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_32
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DOI: https://doi.org/10.1007/978-3-642-22709-7_32
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