Abstract
Protein function prediction is a fundamental problem in modern biology. In this paper, we present the un-normalized graph p-Laplacian semi-supervised learning methods. These methods will be applied to the protein network constructed from the gene expression data to predict the functions of all proteins in the network. These methods are based on the assumption that the labels of two adjacent proteins in the network are likely to be the same. The experiments show that that the un-normalized graph p-Laplacian semi-supervised learning methods are at least as good as the current state of the art method (the un-normalized graph Laplacian based semi-supervised learning method) but often lead to better classification accuracy performance measures.
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Tran, L. (2014). The Un-normalized Graph p-Laplacian Based Semi-supervised Learning Method and Protein Function Prediction Problem. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-319-02741-8_5
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DOI: https://doi.org/10.1007/978-3-319-02741-8_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02740-1
Online ISBN: 978-3-319-02741-8
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