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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
03 May 2011-PLOS ONE
TL;DR: A new concept of global geometric affinity and an accompanying computational scheme to filter the uncertain PPIs, namely, reduce the spurious PPIs and recover the missing PPIs in the network are proposed.
Abstract: Protein-protein interaction (PPI) network analysis presents an essential role in understanding the functional relationship among proteins in a living biological system. Despite the success of current approaches for understanding the PPI network, the large fraction of missing and spurious PPIs and a low coverage of complete PPI network are the sources of major concern. In this paper, based on the diffusion process, we propose a new concept of global geometric affinity and an accompanying computational scheme to filter the uncertain PPIs, namely, reduce the spurious PPIs and recover the missing PPIs in the network. The main concept defines a diffusion process in which all proteins simultaneously participate to define a similarity metric (global geometric affinity (GGA)) to robustly reflect the internal connectivity among proteins. The robustness of the GGA is attributed to propagating the local connectivity to a global representation of similarity among proteins in a diffusion process. The propagation process is extremely fast as only simple matrix products are required in this computation process and thus our method is geared toward applications in high-throughput PPI networks. Furthermore, we proposed two new approaches that determine the optimal geometric scale of the PPI network and the optimal threshold for assigning the PPI from the GGA matrix. Our approach is tested with three protein-protein interaction networks and performs well with significant random noises of deletions and insertions in true PPIs. Our approach has the potential to benefit biological experiments, to better characterize network data sets, and to drive new discoveries.

19 citations

Book ChapterDOI
02 Dec 2005
TL;DR: A new network growth model is introduced that takes into account the sequence similarity between pairs of proteins (as a binary relationship) as well as their interactions and captures not only the k-hop degree distribution of the yeast protein interaction network for all k > 0, but it also captures the 1-hopdegree distribution of a sequence similarity network, which seems to form a power law.
Abstract: Protein-protein interaction networks, particularly that of the yeast S. Cerevisiae, have recently been studied extensively. These networks seem to satisfy the small world property and their (1-hop) degree distribution seems to form a power law. More recently, a number of duplication based random graph models have been proposed with the aim of emulating the evolution of protein-protein interaction networks and satisfying these two graph theoretical properties. In this paper, we show that the proposed model of Pastor-Satorras et al. does not generate the power law degree distribution with exponential cutoff as claimed and the more restrictive model by Chung et al. cannot be interpreted unconditionally. It is possible to slightly modify these models to ensure that they generate a power law degree distribution. However, even after this modification, the more general k-hop degree distribution achieved by these models, for k > 1, are very different from that of the yeast proteome network. We address this problem by introducing a new network growth model that takes into account the sequence similarity between pairs of proteins (as a binary relationship) as well as their interactions. The new model captures not only the k-hop degree distribution of the yeast protein interaction network for all k > 0, but it also captures the 1-hop degree distribution of the sequence similarity network, which again seems to form a power law.

19 citations

Book ChapterDOI
Chuan Lin1, Young-Rae Cho1, Woochang Hwang1, Pengjun Pei1, Aidong Zhang1 
23 May 2007-Scopus
TL;DR: This chapter will begin with a brief introduction of the properties of protein-protein interaction networks, including a review of the data which has been generated by both experimental and computational approaches.

19 citations

Journal ArticleDOI
TL;DR: Bi-clustering tree ensembles are proposed as a new machine learning method for network inference, extending the traditional tree-ensemble models to the global network setting and demonstrating the potential of the model in predicting non-reported interactions.
Abstract: Network inference is crucial for biomedicine and systems biology. Biological entities and their associations are often modeled as interaction networks. Examples include drug protein interaction or gene regulatory networks. Studying and elucidating such networks can lead to the comprehension of complex biological processes. However, usually we have only partial knowledge of those networks and the experimental identification of all the existing associations between biological entities is very time consuming and particularly expensive. Many computational approaches have been proposed over the years for network inference, nonetheless, efficiency and accuracy are still persisting open problems. Here, we propose bi-clustering tree ensembles as a new machine learning method for network inference, extending the traditional tree-ensemble models to the global network setting. The proposed approach addresses the network inference problem as a multi-label classification task. More specifically, the nodes of a network (e.g., drugs or proteins in a drug-protein interaction network) are modelled as samples described by features (e.g., chemical structure similarities or protein sequence similarities). The labels in our setting represent the presence or absence of links connecting the nodes of the interaction network (e.g., drug-protein interactions in a drug-protein interaction network). We extended traditional tree-ensemble methods, such as extremely randomized trees (ERT) and random forests (RF) to ensembles of bi-clustering trees, integrating background information from both node sets of a heterogeneous network into the same learning framework. We performed an empirical evaluation, comparing the proposed approach to currently used tree-ensemble based approaches as well as other approaches from the literature. We demonstrated the effectiveness of our approach in different interaction prediction (network inference) settings. For evaluation purposes, we used several benchmark datasets that represent drug-protein and gene regulatory networks. We also applied our proposed method to two versions of a chemical-protein association network extracted from the STITCH database, demonstrating the potential of our model in predicting non-reported interactions. Bi-clustering trees outperform existing tree-based strategies as well as machine learning methods based on other algorithms. Since our approach is based on tree-ensembles it inherits the advantages of tree-ensemble learning, such as handling of missing values, scalability and interpretability.

19 citations

Book ChapterDOI
27 Oct 2010
TL;DR: This work argues that an expertise mining algorithm must consider a person's interest and activity level in a certain collaboration context and proposes an approach based on the PageRank algorithm enhanced by techniques to incorporate contextual link information.
Abstract: Online tools for collaboration and social platforms have become omnipresent in Web-based environments Interests and skills of people evolve over time depending in performed activities and joint collaborations We believe that ranking models for recommending experts or collaboration partners should not only rely on profiles or skill information that need to be manually maintained and updated by the user In this work we address the problem of expertise mining based on performed interactions between people We argue that an expertise mining algorithm must consider a person's interest and activity level in a certain collaboration context Our approach is based on the PageRank algorithm enhanced by techniques to incorporate contextual link information An approach comprising two steps is presented First, offline analysis of human interactions considering tagged interaction links and second composition of ranking scores based on preferences We evaluate our approach using an email interaction network

19 citations


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