A Simplified Linear Model for Reconstruction of Gene Regulatory Networks
نویسندگان
چکیده
Many inference methods have been proposed to reconstruct unknown Gene Regulatory Networks (GRN) using microarray datasets. State Space Model (SSM) is a method that can be used to infer GRNs from a time series dataset. However, there exist two difficulties in SSM when it is applied for GRN reconstruction: how to estimate initial values of parameters and how to learn the genegene interactions in the networks. We introduce a Simplified Linear Model (SLM) which uses Principle Component Analysis (PCA) to reduce the dimension of the dataset and the noise. A total of 20 synthetic GRNs were generated by GeneNetWeaver and they were used to test the average inference accuracies of both SSM and SLM. Our results show that SLM is more stable with different length of hidden variables and it can be applied to a smaller dataset. The proposed SLM significantly improved the performance of GRN reconstruction.
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