CL-RBF：An Improved ML-RBF Method for Prediction of Protein Subcellular Location

Abstract:

CL-RBF algorithm was proposed to predict the protein subcellular localization，which is considered about cluster results within one label and between different labels of the ML-RBF method. Silhouette coefficient was introduced to get the optimal number of centroids on hidden layer. The previous approach only considered optimization of clustering algorithms within the same label. In this paper，larger distance between two centroids which were generated from two labels was taken into account，when there were less samples covering these two labels. Besides，gradient descent algorithm was used to adjust the parameters. The final adjustment was made by analyzing the distance between train samples，the hidden centers obtained by label L and the clustering centers not belonging to label L. Bag of words and AAC method were employed to extract the feature of protein sequence. Compared with the methods which have been introduced previously for bacterial protein subcellular localization prediction via 10-fold cross-validation test，the new predictor performed more powerful and flexible on four different multi-label datasets.The prediction server was available on https://njau.applinzi.com/homepage_final.jsp.