Constructing higher-order miRNA-mRNA interaction networks in prostate cancer via hypergraph-based learning.
Constructing higher-order miRNA-mRNA interaction networks in prostate cancer via hypergraph-based learning.
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DOI：
10.1186/1752-0509-7-47
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发表时间：
2013-06-19
影响因子：
--
通讯作者：
Zhang BT
中科院分区：
文献类型：
--
作者：
Kim SJ;Ha JW;Zhang BT
遗传因子如microRNA（miRNAs）和mRNAs的失调已被广泛证明与癌症进展和发展相关。特别是，miRNAs和mRNAs合作影响生物过程，包括肿瘤发生。miRNA-mRNA相互作用的复杂性是识别其协同调节作用和功能效应的主要障碍。因此，通过对这些复杂关系进行计算建模，可以推断出复杂生物过程背后的基因相互作用网络。我们提出了一种数据驱动的超图结构方法，用于从癌症基因组图谱构建高阶miRNA-mRNA相互作用网络。该模型明确地描述了遗传因素之间的高阶关系，从中可以确定生物过程中的合作基因活动。该模型通过结构和参数的迭代学习来学习。结构学习通过生成代表复杂miRNA-mRNA模块的假定超边来有效地构建超图结构。它采用了基于信息论标准的进化方法。在参数学习阶段，利用梯度下降法更新超图的超边权值，从而对超图进行优化。从该模型中，我们产生了生物学相关的高阶相互作用网络，显示了原发性和转移性前列腺癌的特性，作为潜在的miRNA-mRNA调控回路的候选者。我们的方法侧重于潜在的癌症特异性相互作用，反映了表达谱中miRNA和mRNA之间的高阶关系。构建的miRNA-mRNA相互作用网络显示致癌或肿瘤抑制特征，已知其与前列腺癌进展直接相关。因此，基于超图的模型可以辅助癌症分子发病机制的假设制定。
Dysregulation of genetic factors such as microRNAs (miRNAs) and mRNAs has been widely shown to be associated with cancer progression and development. In particular, miRNAs and mRNAs cooperate to affect biological processes, including tumorigenesis. The complexity of miRNA-mRNA interactions presents a major barrier to identifying their co-regulatory roles and functional effects. Thus, by computationally modeling these complex relationships, it may be possible to infer the gene interaction networks underlying complicated biological processes. We propose a data-driven, hypergraph structural method for constructing higher-order miRNA-mRNA interaction networks from cancer genomic profiles. The proposed model explicitly characterizes higher-order relationships among genetic factors, from which cooperative gene activities in biological processes may be identified. The proposed model is learned by iteration of structure and parameter learning. The structure learning efficiently constructs a hypergraph structure by generating putative hyperedges representing complex miRNA-mRNA modules. It adopts an evolutionary method based on information-theoretic criteria. In the parameter learning phase, the constructed hypergraph is refined by updating the hyperedge weights using the gradient descent method. From the model, we produce biologically relevant higher-order interaction networks showing the properties of primary and metastatic prostate cancer, as candidates of potential miRNA-mRNA regulatory circuits. Our approach focuses on potential cancer-specific interactions reflecting higher-order relationships between miRNAs and mRNAs from expression profiles. The constructed miRNA-mRNA interaction networks show oncogenic or tumor suppression characteristics, which are known to be directly associated with prostate cancer progression. Therefore, the hypergraph-based model can assist hypothesis formulation for the molecular pathogenesis of cancer.
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DOI：
10.1093/bioinformatics/btq395
发表时间：
2010-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Bonnet E;Michoel T;Van de Peer Y
通讯作者：
Van de Peer Y
影响因子：
8.8
作者：
Kojima, S.;Chiyomaru, T.;Kawakami, K.;Yoshino, H.;Enokida, H.;Nohata, N.;Fuse, M.;Ichikawa, T.;Naya, Y.;Nakagawa, M.;Seki, N.
通讯作者：
Seki, N.
影响因子：
5.8
作者：
Joung, Je-Gun;Hwang, Kyu-Baek;Zhang, Byoung-Tak
通讯作者：
Zhang, Byoung-Tak
影响因子：
64.8
作者：
Hartwell, LH;Hopfield, JJ;Murray, AW
通讯作者：
Murray, AW
miR2Disease：人类疾病中 microRNA 失调的手动数据库
影响因子：
14.9
作者：
Jiang Q;Wang Y;Hao Y;Juan L;Teng M;Zhang X;Li M;Wang G;Liu Y
通讯作者：
Liu Y