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Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.


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Journal ArticleDOI
TL;DR: This new result allows us for example to partly solve the topology of the genetic regulatory network ruling the flowering in Arabidopsis thaliana.
Abstract: This paper deals with the problem of reconstruction of the intergenic interaction graph from the raw data of genetic co-expression coming with new technologies of bio-arrays (DMA-arrays, protein-arrays, etc.). These new imaging devices in general only give information about the asymptotical part (fixed configurations of co-expression or limit cycles of such configurations) of the dynamical evolution of the regulatory networks (genetic and/or proteic) underlying the functioning of living systems. Extracting the casual structure and interaction coefficients of a gene interaction network from the observed configurations is a complex problem. But if all the fixed configurations are supposedly observed and if they are factorizable into two or more subsets of values, then the interaction graph possesses as many connected components as the number of factors and the solution is obtained in polynomial time. This new result allows us for example to partly solve the topology of the genetic regulatory network ruling the flowering in Arabidopsis thaliana.

17 citations

Journal ArticleDOI
TL;DR: Polar Mapper facilitates the system-level analysis of mRNA expression data in the context of the underlying protein interaction network and comparison of the yeast oxidative stress and heat shock gene expression responses are addressed as case studies.
Abstract: Polar Mapper is a computational application for exposing the architecture of protein interaction networks. It facilitates the system-level analysis of mRNA expression data in the context of the underlying protein interaction network. Preliminary analysis of a human protein interaction network and comparison of the yeast oxidative stress and heat shock gene expression responses are addressed as case studies.

17 citations

Journal ArticleDOI
TL;DR: The study reveals that graph embedding and ensemble learning based method is efficient for integrating heterogeneous information derived from lncRNA-miRNA interaction network and can achieve better performance on LMI prediction task.
Abstract: Researchers discover LncRNA–miRNA regulatory paradigms modulate gene expression patterns and drive major cellular processes. Identification of lncRNA-miRNA interactions (LMIs) is critical to reveal the mechanism of biological processes and complicated diseases. Because conventional wet experiments are time-consuming, labor-intensive and costly, a few computational methods have been proposed to expedite the identification of lncRNA-miRNA interactions. However, little attention has been paid to fully exploit the structural and topological information of the lncRNA-miRNA interaction network. In this paper, we propose novel lncRNA-miRNA prediction methods by using graph embedding and ensemble learning. First, we calculate lncRNA-lncRNA sequence similarity and miRNA-miRNA sequence similarity, and then we combine them with the known lncRNA-miRNA interactions to construct a heterogeneous network. Second, we adopt several graph embedding methods to learn embedded representations of lncRNAs and miRNAs from the heterogeneous network, and construct the ensemble models using two ensemble strategies. For the former, we consider individual graph embedding based models as base predictors and integrate their predictions, and develop a method, named GEEL-PI. For the latter, we construct a deep attention neural network (DANN) to integrate various graph embeddings, and present an ensemble method, named GEEL-FI. The experimental results demonstrate both GEEL-PI and GEEL-FI outperform other state-of-the-art methods. The effectiveness of two ensemble strategies is validated by further experiments. Moreover, the case studies show that GEEL-PI and GEEL-FI can find novel lncRNA-miRNA associations. The study reveals that graph embedding and ensemble learning based method is efficient for integrating heterogeneous information derived from lncRNA-miRNA interaction network and can achieve better performance on LMI prediction task. In conclusion, GEEL-PI and GEEL-FI are promising for lncRNA-miRNA interaction prediction.

17 citations

Proceedings ArticleDOI
Jianxin Wang1, Xiaoqing Peng1, Min Li1, Yong Luo1, Yi Pan1 
12 Nov 2011
TL;DR: This paper proposed a method to identify active time points for each protein in a cellular process or cycle by using a 3-sigma principle to compute an active threshold for each gene according to the characteristics of its expression curve and constructed an active protein interaction network (APPI).
Abstract: In recent years, more and more attentions are focused on modelling and analyzing dynamic network. Some researchers attempted to extract dynamic network by combining the dynamic information from gene expression data or sub cellular localization data with protein network. However, the dynamics of proteins' presence does not guarantee the dynamics of interactions, since the presence of a protein does not indicate the protein's activity. The activity of a protein is closely connected with its function. Thus only the dynamics of proteins activity ensure the dynamics of interaction. The gene expression of a cellular process or cycle carries more information than only the dynamics of proteins' presence. We assume that a protein is active when its expression values are near its maximum expression value, since the expression quantity will decrease after it has performed its function that leads a feedback for controlling the expression quantity. In this paper, we proposed a method to identify active time points for each protein in a cellular process or cycle by using a 3-sigma principle to compute an active threshold for each gene according to the characteristics of its expression curve. Combined the activity information and protein interaction network, we can construct an active protein interaction network (APPI). To demonstrate the efficiency of APPI network model, we applied it on complex detection. Compared with single threshold time series networks, APPI network achieves a better performance on protein complex prediction.

17 citations

Proceedings ArticleDOI
01 Dec 2010
TL;DR: NetLoc, a novel diffusion kernel-based logistic regression (KLR) algorithm for predicting protein subcellular localization using four types of protein networks including physical protein-protein interaction (PPPI) networks, genetic PPI networks (GPPI), mixed P PI networks (MPPI), and co-expression networks (COEXP) is presented.
Abstract: Recent studies showed that protein-protein interaction network based features can significantly improve the prediction of protein subcellular localization. However, it is unclear whether network prediction models or other types of protein-protein correlation networks would also improve localization prediction. We present NetLoc, a novel diffusion kernel-based logistic regression (KLR) algorithm for predicting protein subcellular localization using four types of protein networks including physical protein-protein interaction (PPPI) networks, genetic PPI networks (GPPI), mixed PPI networks (MPPI), and co-expression networks (COEXP). We applied NetLoc to yeast protein localization prediction. The results showed that protein networks can provide rich information for protein localization prediction, achieving prediction performance up to AUC score of 0.93. We also showed that networks with high connectivity and high percentage of interacting protein pairs targeting the same location lead to better prediction performance. We found that physical PPPI is better than GPPI which is better than COEXP in terms of localization prediction. The prediction performance (AUC) using the yeast PPPI network ranges between 0.71 and 0.93 for 7 locations. Compared to the previous network feature based prediction algorithm which achieved AUC scores of (0.49 and 0.52) on the yeast PPI network of the DIP database, NetLoc achieved significantly better overall performance with the AUC of 0.74.

16 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202337
202290
2021183
2020221
2019201
2018163