Tibshirani, Regression Shrinkage and Selection Via the Lasso, J. Califano, Reverse engineering of regulatory networks in human B cells, Nat. Liu, Differential Coexpression Network Analysis for Gene Expression Data, in Computational Systems Biology: Methods and Protocols (ed T. Hu, Mathematical modeling and computational prediction of cancer drug resistance, Briefings Bioinf., 19 (2018), 1382-1399. Dynamic gene regulatory network reconstruction and analysis based on clinical transcriptomic data of colorectal cancer. Our study demonstrated that reconstructing dynamic GRNs based on clinical transcriptomic profiling allows us to detect the dynamic trend of gene regulation as well as reveal critical genes for cancer development which may be important candidates of master regulators for further experimental test.Ĭitation: Ancheng Deng, Xiaoqiang Sun. Several important genes were revealed based on the rewiring of the reconstructed GRNs. Dynamic GRNs at four different stages of colorectal cancer were reconstructed and analyzed. We then developed a dynamical system-based optimization method to infer dynamic GRNs by incorporating mutual information-based network sparsification and a dynamic cascade technique into an ordinary differential equations model. We combined multiple sets of clinical transcriptomic data of colorectal cancer patients and employed a supervised approach to select initial gene set for network construction. In this study, we reconstruct the stage-specific gene regulatory networks (GRNs) for colorectal cancer to understand dynamic changes of gene regulations along different disease stages. Inferring dynamic regulatory networks that rewire at different stages is a reasonable way to understand the mechanisms underlying cancer development.
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