Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data\nthrough computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been\ninaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect\ninteraction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion\non specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies\nwere not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors.\nHence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid\nwrongly inferring of an indirect interaction (A ââ? â?? B ââ? â?? C) as a direct interaction (A ââ? â?? C). Since the number of observations of\nthe real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random\nsubnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to\nassess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed\nin this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley\ncollinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks\nobtained satisfactory results, with AUROC values above 0.5.
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