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MINING PROTEIN REGULATORY RELATIONSHIPS USING NEURAL NETWORK METHODS FOR EARLY PREDICTION OF SARS
https://doi.org/10.1142/S0218126609005745Cited by:1 (Source: Crossref)
Abstract
This paper proposes to model protein regulation networks associated with severe acute respiratory syndrome (SARS) for early prediction of SARS. In the approach, specific to a patient group, a regulatory network is simulated using a fully-connected neural network and is optimized towards minimizing a novel energy function that is defined as a measure of disagreement between the input and output of the network. The nonlinear version of the network is achieved by applying a sigmoid function. Experimental results show that the proposed approaches can capture regulatory patterns associated with SARS and efficiently implement early prediction of SARS.
