A summary of SLapRLS.

<p>A summary of SLapRLS.</p

Compared AUC values of the four algorithms: SLapRLS, SVM, BDT and KNN.

<p>Compared AUC values of the four algorithms: SLapRLS, SVM, BDT and KNN.</p

ROC curves of different algorithms.

<p>ROC curves of kinase Erk2, Erk1, CDC2 and PKC alpha achieved by four different algorithms are plotted. The red line, blue line, yellow line and cyan line represent SLapRLS, SVM, BDT and KNN, respectively.</p

Kinase Identification with Supervised Laplacian Regularized Least Squares

<div><p>Phosphorylation is catalyzed by protein kinases and is irreplaceable in regulating biological processes. Identification of phosphorylation sites with their corresponding kinases contributes to the understanding of molecular mechanisms. Mass spectrometry analysis of phosphor-proteomes generates a large number of phosphorylated sites. However, experimental methods are costly and time-consuming, and most phosphorylation sites determined by experimental methods lack kinase information. Therefore, computational methods are urgently needed to address the kinase identification problem. To this end, we propose a new kernel-based machine learning method called Supervised Laplacian Regularized Least Squares (SLapRLS), which adopts a new method to construct kernels based on the similarity matrix and minimizes both structure risk and overall inconsistency between labels and similarities. The results predicted using both Phospho.ELM and an additional independent test dataset indicate that SLapRLS can more effectively identify kinases compared to other existing algorithms.</p></div

Procedure of this work.

<p>Firstly, label dataset are derived from Phospho.ELM, and it is split into train dataset and test dataset. Secondly, the model is developed using train dataset and its similarity matrix with SLapRLS, with which the predicted result of test dataset is achieved. Additionally, an independent test dataset is used. The model that predicts the independent dataset is developed with all the label dataset.</p