Using the nonlinear dimensionality reduction method for the prediction of subcellular localization of Gram-negative bacterial proteins |
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Authors: | Tong Wang Jie Yang |
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Institution: | 1. Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, 200240, China
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Abstract: | One of the central problems in computational biology is protein function identification in an automated fashion. A key step
to achieve this is predicting to which subcellular location the protein belongs, since protein localization correlates closely
with its function. A wide variety of methods for protein subcellular localization prediction have been proposed over recent
years. Linear dimensionality reduction (DR) methods have been introduced to address the high-dimensionality problem by transforming
the representation of protein sequences. However, this approach is not suitable for some complex biological systems that have
nonlinear characteristics. Herein, we use nonlinear DR methods such as the kernel DR method to capture the nonlinear characteristics
of a high-dimensional space. Then, the K-nearest-neighbor (K-NN) classifier is employed to identify the subcellular localization of Gram-negative bacterial proteins based on their reduced
low-dimensional features. Experimental results thus obtained are quite encouraging, indicating that the applied nonlinear
DR method is effective to deal with this complicated problem of predicting subcellular localization of Gram-negative bacterial
proteins. An online web server for predicting subcellular location of Gram-negative bacterial proteins is available at . |
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Keywords: | |
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