Fusion of Gene Regulatory and Protein Interaction Networks Using Skip-Chain Models

机译：使用跳过链模型的基因调控和蛋白质相互作用网络的融合。

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Inference of Gene Regulatory Networks (GRN) is important in understanding signal transduction pathways. This involves predicting the correct sequence of interactions and identifying all interacting genes. Using only gene expression data is insufficient, so additional sources of data like protein-protein interaction network (PPIN) are required. In this paper, we model time delayed interactions using a skip-chain model which finds missing edges between non-consecutive time points based on PPIN. Highest Viterbi approximation is used to select skip-edges. The k-skip validation model checks for k missing genes between a predicted interaction, giving us advantages of validation as well as expansion of GRN. The method is demonstrated on a cell-division cycle data of S. cerevisiae (yeast). Comparison of the present method, with a previous approach of modeling PPIN by using a Gibbs prior are given.

机译：基因调控网络（GRN）的推论对理解信号转导途径很重要。这涉及预测正确的相互作用序列并鉴定所有相互作用的基因。仅使用基因表达数据是不够的，因此需要其他数据源，例如蛋白质-蛋白质相互作用网络（PPIN）。在本文中，我们使用跳过链模型对时间延迟交互进行建模，该跳过链模型基于PPIN查找非连续时间点之间的缺失边。最高维特比近似值用于选择跳边。 k-跳过验证模型检查预测的相互作用之间的k个缺失基因，为我们提供了验证以及GRN扩展的优势。该方法在酿酒酵母（酵母）的细胞分裂周期数据中得到证明。给出了本方法与使用Gibbs先验对PPIN建模的先前方法的比较。

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《Pattern Recognition in Bioinformatics》|2008年|214-224|共11页
会议地点 Melbourne(AU);Melbourne(AU)
作者
Iti Chaturvedi; Jagath C. Rajapakse;
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作者单位

Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore;

Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore Singapore-MIT Alliance, Singapore Department of Biological Engineering, Massachusetts Institute of Technology, USA;

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原文格式 PDF
正文语种 eng
中图分类生物工程学（生物技术）;
关键词
dynamic Bayesian networks; gene regulatory networks; higher-order markov chains; protein-protein interactions; viterbi algo-rithm;

机译：动态贝叶斯网络；基因调控网络；高阶马尔可夫链；蛋白质相互作用维特比算法;
入库时间 2022-08-26 13:51:20

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3. Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks [J] . Wenting Liu, Jagath C. Rajapakse BMC Systems Biology . 2019,第2期

机译：融合基因表达和传递性蛋白质-蛋白质相互作用以推断基因调控网络
4. Fusion of Gene Regulatory and Protein Interaction Networks Using Skip-Chain Models [C] . Iti Chaturvedi, Jagath C. Rajapakse IAPR International Conference on Pattern Recognition in Bioinformatics . 2008

机译：使用跳过链模型融合基因调节和蛋白质相互作用网络
5. Comparative functional analysis and identification of regulatory control in gene networks using the leucine-responsive regulatory protein and its regulon as a model system. [D] . Lintner, Robert E. 2007

机译：使用亮氨酸反应性调节蛋白及其调节子作为模型系统，对基因网络中的调节控制进行比较功能分析和鉴定。
6. Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks [O] . Wenting Liu, Jagath C. Rajapakse 2019

机译：融合基因表达和传递性蛋白质-蛋白质相互作用以推断基因调控网络
7. Automaton Models of Computational Genetic Regulatory Networks with Combinatorial Gene-Protein Logical Interactions [O] . Peter C. Y. Chen, Yulin Weng 2010

机译：具有组合基因 - 蛋白质逻辑相互作用的计算基因调控网络的自动机模型

Fusion of Gene Regulatory and Protein Interaction Networks Using Skip-Chain Models

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