Learning and identifying the crucial proteins in signal transduction networks by a novel method

To identify the crucial proteins in signal transduction networks, the first thing we must do is to know the protein's importance in signaling transduction networks. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. We developed a novel method to evaluate the importance of proteins in signal transduction networks based on the concept of Minimax Distance Metric algorithm (MDMa). A MDMa in signal transduction networks refers to a minimax distance metric set of proteins that can propagate the signal from input to output. We applied this method to the large signal transduction network in the small cell lung cancer. Significant correlations were found. The useful features were observed in signal transduction networks that allow the prediction of the essentiality and conservation of proteins. Experiments show that the above methods are used effectively to deal with this complicated problem proposed in this paper.

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