Analyzing Differential Regulatory Networks Modulated by Continuous-State Genomic Features in Glioblastoma Multiforme

Yu Chiao Chiu, Tzu Hung Hsiao, Li Ju Wang, Yidong Chen, Eric Y. Chuang

    Research output: Contribution to journalArticlepeer-review

    2 Scopus citations


    Gene regulatory networks are a global representation of complex interactions between molecules that dictate cellular behavior. Study of a regulatory network modulated by single or multiple modulators' expression levels, including microRNAs (miRNAs) and transcription factors (TFs), in different conditions can further reveal the modulators' roles in diseases such as cancers. Existing computational methods for identifying such modulated regulatory networks are typically carried out by comparing groups of samples dichotomized with respect to the modulator status, ignoring the fact that most biological features are intrinsically continuous variables. Here, we devised a sliding window-based regression scheme and proposed the Regression-based Inference of Modulation (RIM) algorithm to infer the dynamic gene regulation modulated by continuous-state modulators. We demonstrated the improvement in performance as well as computation efficiency achieved by RIM. Applying RIM to genome-wide expression profiles of 520 glioblastoma multiforme (GBM) tumors, we investigated miRNA- and TF-modulated gene regulatory networks and showed their association with dynamic cellular processes and brain-related functions in GBM. Overall, the proposed algorithm provides an efficient and robust scheme for comprehensively studying modulated gene regulatory networks.

    Original languageEnglish (US)
    Article number7769225
    Pages (from-to)1754-1764
    Number of pages11
    JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
    Issue number6
    StatePublished - Nov 1 2018


    • Differential regulatory networks
    • competing endogenous RNA
    • gene modulation
    • glioblastoma multiforme
    • transcription factor

    ASJC Scopus subject areas

    • Biotechnology
    • Genetics
    • Applied Mathematics


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