A Novel Model Integration Network Inference Algorithm with Clustering and Hub Genes Finding

Gene regulatory network Inference with high accuracy based on gene expression data sets is one of the most challenging problems in computational biology. To improve the accuracy of gene regulatory network inference and find hub genes, we proposed a novel model integration network inference method with clustering and hub genes finding called MINICHG. The method is divided into three main steps: (1) using single models inference results based on three machine learning algorithms to construct feature matrix; (2) using k-means to cluster gene pairs according to feature matrix; (3) hub genes finding. MINICHG integrates RF(Random Forest), GBDT (Gradient Boosting Decision Tree) and Pearson Correlation results with a novel weighted strategy in a semi-unsupervised way. The designed optimization scheme in MINICHG considering sparse gold standard data characteristics is suitable for most gene regulatory network reconstruction. We evaluated the proposed method on simulated data sets from five Dream4 multifactorial data sets and Dream5 in silico data set and real data set from E.coli. The performance was better than other network inference methods with high accuracy and robustness.