Advances in Bioinformatics: Computational and Statistical Approaches for Modeling of Proteomic and Genomic Networks
Mario Flores,1 Tzu-Hung Hsiao,2 Yu-Chiao Chiu,3 Eric Y. Chuang,3 Yufei Huang,1 and Yidong Chen2,4
Abstract
Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed by a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to a new RNA regulation mechanism, competitive endogenous RNA (ceRNA), into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of “modulation” can adapt to various biological mechanisms to discover the novel gene regulation mechanisms.
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