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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
20 Nov 2009-PLOS ONE
TL;DR: The contribution of pure amino acid composition (AAC) for protein interaction prediction is explored, which is a simple, yet powerful feature that can be used alone or in conjunction with protein domains to predict new and validate existing interactions.
Abstract: Background Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information. Results AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins. Conclusion AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains.

74 citations

Journal ArticleDOI
TL;DR: Experimental results on five large protein interaction networks demonstrated that compared to state-of-the-art protein complex detection algorithms, the proposed algorithm outperformed them in terms of both effectiveness and efficiency.
Abstract: Protein complexes are crucial in improving our understanding of the mechanisms employed by proteins. Various computational algorithms have thus been proposed to detect protein complexes from protein interaction networks. However, given massive protein interactome data obtained by high-throughput technologies, existing algorithms, especially those with additionally consideration of biological information of proteins, either have low efficiency in performing their tasks or suffer from limited effectiveness. For addressing this issue, this work proposes to detect protein complexes from a protein interaction network with high efficiency and effectiveness. To do so, the original detection task is first formulated into an optimization problem according to the intuitive properties of protein complexes. After that, the framework of alternating direction method of multipliers is applied to decompose this optimization problem into several subtasks, which can be subsequently solved in a separate and parallel manner. An algorithm for implementing this solution is then developed. Experimental results on five large protein interaction networks demonstrated that compared to state-of-the-art protein complex detection algorithms, our algorithm outperformed them in terms of both effectiveness and efficiency. Moreover, as number of parallel processes increases, one can expect an even higher computational efficiency for the proposed algorithm with no compromise on effectiveness.

74 citations

Journal ArticleDOI
30 Jul 2010-PLOS ONE
TL;DR: Network features were found to be most important for accurate prediction and can significantly improve the prediction performance, and the results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association.
Abstract: Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies.

73 citations

Journal ArticleDOI
TL;DR: CanSAR can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets.
Abstract: canSAR is a fully integrated cancer research and drug discovery resource developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface at http://cansar.icr.ac.uk provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data.

73 citations

Journal ArticleDOI
TL;DR: A novel Graph Convolutional Network (GCN) based framework for predicting human Microbe-Drug Associations, named GCNMDA is proposed, which consistently achieved better performance than seven state-of-the-art methods.
Abstract: MOTIVATION: Human microbes play critical roles in drug development and precision medicine How to systematically understand the complex interaction mechanism between human microbes and drugs remains a challenge nowadays Identifying microbe-drug associations can not only provide great insights into understanding the mechanism, but also boost the development of drug discovery and repurposing Considering the high cost and risk of biological experiments, the computational approach is an alternative choice However, at present, few computational approaches have been developed to tackle this task RESULTS: In this work, we leveraged rich biological information to construct a heterogeneous network for drugs and microbes, including a microbe similarity network, a drug similarity network and a microbe-drug interaction network We then proposed a novel graph convolutional network (GCN)-based framework for predicting human Microbe-Drug Associations, named GCNMDA In the hidden layer of GCN, we further exploited the Conditional Random Field (CRF), which can ensure that similar nodes (ie microbes or drugs) have similar representations To more accurately aggregate representations of neighborhoods, an attention mechanism was designed in the CRF layer Moreover, we performed a random walk with restart-based scheme on both drug and microbe similarity networks to learn valuable features for drugs and microbes, respectively Experimental results on three different datasets showed that our GCNMDA model consistently achieved better performance than seven state-of-the-art methods Case studies for three microbes including SARS-CoV-2 and two antimicrobial drugs (ie Ciprofloxacin and Moxifloxacin) further confirmed the effectiveness of GCNMDA in identifying potential microbe-drug associations AVAILABILITY AND IMPLEMENTATION: Python codes and dataset are available at: https://githubcom/longyahui/GCNMDA SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

73 citations


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