scholarly journals Identifying communities from multiplex biological networks

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1525 ◽  
Author(s):  
Gilles Didier ◽  
Christine Brun ◽  
Anaïs Baudot
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Biological Network ◽  
Random Networks ◽  
Functional Modules ◽  
Source Codes ◽  
Network Layers ◽  
Or Gene ◽  
Multiple Network ◽  
User Friendly

Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multiple network sources. By simulating random networks, we demonstrated that the multiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functional interactions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity of biological networks leads to better-defined functional modules. A user-friendly graphical software to detect communities from multiplex networks, and corresponding C source codes, are available at GitHub (https://github.com/gilles-didier/MolTi).

HiSCF: leveraging higher-order structures for clustering analysis in biological networks

2020 ◽  
Author(s):  
Lun Hu ◽  
Jun Zhang ◽  
Xiangyu Pan ◽  
Hong Yan ◽  
Zhu-Hong You
Keyword(s):  
Biological Networks ◽  
Clustering Analysis ◽  
Biological Network ◽  
Higher Order ◽  
Supplementary Information ◽  
Network Motifs ◽  
Functional Modules ◽  
Connectivity Patterns ◽  
Biological Entities ◽  
Insight Into

Abstract Motivation Clustering analysis in a biological network is to group biological entities into functional modules, thus providing valuable insight into the understanding of complex biological systems. Existing clustering techniques make use of lower-order connectivity patterns at the level of individual biological entities and their connections, but few of them can take into account of higher-order connectivity patterns at the level of small network motifs. Results Here, we present a novel clustering framework, namely HiSCF, to identify functional modules based on the higher-order structure information available in a biological network. Taking advantage of higher-order Markov stochastic process, HiSCF is able to perform the clustering analysis by exploiting a variety of network motifs. When compared with several state-of-the-art clustering models, HiSCF yields the best performance for two practical clustering applications, i.e. protein complex identification and gene co-expression module detection, in terms of accuracy. The promising performance of HiSCF demonstrates that the consideration of higher-order network motifs gains new insight into the analysis of biological networks, such as the identification of overlapping protein complexes and the inference of new signaling pathways, and also reveals the rich higher-order organizational structures presented in biological networks. Availability and implementation HiSCF is available at https://github.com/allenv5/HiSCF. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Handling Noise in Protein Interaction Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Fernanda B. Correia ◽  
Edgar D. Coelho ◽  
José L. Oliveira ◽  
Joel P. Arrais
Keyword(s):  
Network Topology ◽  
Protein Interactions ◽  
Biological Networks ◽  
Homo Sapiens ◽  
Random Networks ◽  
Protein Protein Interactions ◽  
Ppi Networks ◽  
Reference Set ◽  
Human Networks ◽  
Random Removal

Protein-protein interactions (PPIs) can be conveniently represented as networks, allowing the use of graph theory for their study. Network topology studies may reveal patterns associated with specific organisms. Here, we propose a new methodology to denoise PPI networks and predict missing links solely based on the network topology, the organization measurement (OM) method. The OM methodology was applied in the denoising of the PPI networks of two Saccharomyces cerevisiae datasets (Yeast and CS2007) and one Homo sapiens dataset (Human). To evaluate the denoising capabilities of the OM methodology, two strategies were applied. The first strategy compared its application in random networks and in the reference set networks, while the second strategy perturbed the networks with the gradual random addition and removal of edges. The application of the OM methodology to the Yeast and Human reference sets achieved an AUC of 0.95 and 0.87, in Yeast and Human networks, respectively. The random removal of 80% of the Yeast and Human reference set interactions resulted in an AUC of 0.71 and 0.62, whereas the random addition of 80% interactions resulted in an AUC of 0.75 and 0.72, respectively. Applying the OM methodology to the CS2007 dataset yields an AUC of 0.99. We also perturbed the network of the CS2007 dataset by randomly inserting and removing edges in the same proportions previously described. The false positives identified and removed from the network varied from 97%, when inserting 20% more edges, to 89%, when 80% more edges were inserted. The true positives identified and inserted in the network varied from 95%, when removing 20% of the edges, to 40%, after the random deletion of 80% edges. The OM methodology is sensitive to the topological structure of the biological networks. The obtained results suggest that the present approach can efficiently be used to denoise PPI networks.

scholarly journals Formal Analysis of Network Motifs

2018 ◽  
Author(s):  
Hillel Kugler ◽  
Sara-Jane Dunn ◽  
Boyan Yordanov
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Formal Analysis ◽  
Building Blocks ◽  
Pulse Generation ◽  
Random Networks ◽  
Network Motifs ◽  
Formal Reasoning ◽  
Dynamical Behaviors ◽  
Stem Cell Pluripotency

AbstractA recurring set of small sub-networks have been identified as the building blocks of biological networks across diverse organisms. These network motifs have been associated with certain dynamical behaviors and define key modules that are important for understanding complex biological programs. Besides studying the properties of motifs in isolation, existing algorithms often evaluate the occurrence frequency of a specific motif in a given biological network compared to that in random networks of similar structure. However, it remains challenging to relate the structure of motifs to the observed and expected behavior of the larger network. Indeed, even the precise structure of these biological networks remains largely unknown. Previously, we developed a formal reasoning approach enabling the synthesis of biological networks capable of reproducing some experimentally observed behavior. Here, we extend this approach to allow reasoning about the requirement for specific network motifs as a way of explaining how these behaviors arise. We illustrate the approach by analyzing the motifs involved in sign-sensitive delay and pulse generation. We demonstrate the scalability and biological relevance of the approach by revealing the requirement for certain motifs in the network governing stem cell pluripotency.

scholarly journals New scaling relation for information transfer in biological networks

2015 ◽  
Vol 12 (113) ◽  
pp. 20150944 ◽  
Author(s):  
Hyunju Kim ◽  
Paul Davies ◽  
Sara Imari Walker
Keyword(s):  
Biological Networks ◽  
Network Architecture ◽  
Information Transfer ◽  
Biological Network ◽  
Random Network ◽  
Causal Structure ◽  
Random Networks ◽  
Scaling Relation ◽  
Scale Free ◽  
Link Type

We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 ( doi:10.1371/journal.pone.0001672 )) and that of the budding yeast Saccharomyces cerevisiae (Li et al . 2004 Proc. Natl Acad. Sci. USA 101, 4781–4786 ( doi:10.1073/pnas.0305937101 )). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös–Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology ( causal structure ) and dynamics ( function ). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties.

scholarly journals Handling Noise in Protein Interaction Networks

2019 ◽  
Author(s):  
Fernanda B. Correia ◽  
Edgar D. Coelho ◽  
José L. Oliveira ◽  
Joel P. Arrais
Keyword(s):  
Network Topology ◽  
Protein Interactions ◽  
Biological Networks ◽  
Homo Sapiens ◽  
Protein Interaction Networks ◽  
Random Networks ◽  
Protein Protein Interactions ◽  
Ppi Networks ◽  
Human Networks ◽  
Random Removal

AbstractProtein-protein interactions (PPI) can be conveniently represented as networks, allowing the use of graph theory in their study. Network topology studies may reveal patterns associated to specific organisms. Here we propose a new methodology to denoise PPI networks and predict missing links solely based on the network topology, the Organization Measurement (OM) method. The OM methodology was applied in the denoising of the PPI networks of two Saccharomyces Cerevisiae datasets (Yeast and CS2007) and one Homo Sapiens dataset (Human). To evaluate the denoising capabilities of OM methodology, two strategies were applied. The first compared its application in random networks and in the reference set networks, while the second perturbed the networks with the gradual random addition and removal of edges. The application of OM methodology to the Yeast and Human reference sets achieved an AUC of 0.95 and 0.87, in Yeast and Human networks, respectively. The random removal of 80% of the Yeast and Human reference sets interactions resulted in an AUC of 0.71 and 0.62, whereas the random addition of 80% interactions resulted in an AUC of 0.75 and 0.72, respectively. Applying the OM methodology to the CS2007 dataset yields an AUC of 0.99. We also perturbed the network of the CS2007 dataset by randomly inserting and removing edges in the same proportions previously described. The false positives identified and removed from the network varied from 97%, when inserting 20% more edges, to 89% when 80% more edges were inserted. The true positives identified and inserted in the network varied from 95% when removing 20% of the edges, to 40% after the random deletion 80% edges. The OM methodology is sensitive to the topological structure of the biological networks. The obtained results suggest that the present approach can efficiently be used to denoise PPI networks.

scholarly journals BIONIC: Biological Network Integration using Convolutions

2021 ◽  
Author(s):  
Duncan T Forster ◽  
Charles Boone ◽  
Gary Bader ◽  
Bo Wang
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Biological Network ◽  
Genetic Interactions ◽  
Protein Protein Interactions ◽  
Network Integration ◽  
Convolutional Network ◽  
Integration Algorithm ◽  
Functional Relationships ◽  
Unsupervised Deep Learning

Biological networks constructed from varied data, including protein-protein interactions, gene expression data, and genetic interactions can be used to map cellular function, but each data type has individual limitations such as bias and incompleteness. Unsupervised network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive result. However, existing unsupervised network integration methods fail to adequately scale to the number of nodes and networks present in genome-scale data and do not handle partial network overlap. To address these issues, we developed an unsupervised deep learning-based network integration algorithm that incorporates recent advances in reasoning over unstructured data, namely the graph convolutional network (GCN), and can effectively learn dependencies between any input network, such as those composed of protein-protein interactions, gene co-expression, or genetic interactions. Our method, BIONIC (Biological Network Integration using Convolutions), learns features which contain substantially more functional information compared to existing approaches, linking genes that share diverse functional relationships, including co-complex and shared bioprocess annotation. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome.

Analysis of Protein-Protein Interaction Networks through Computational Approaches

2020 ◽  
Vol 27 (4) ◽  
pp. 265-278 ◽  
Author(s):  
Ying Han ◽  
Liang Cheng ◽  
Weiju Sun
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Interaction Networks ◽  
Computational Techniques ◽  
Cellular Functions ◽  
Protein Protein Interaction ◽  
Comprehensive Information ◽  
Protein Interaction Prediction ◽  
Or Gene ◽  
Experimental Findings

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

2019 ◽  
Vol 19 (6) ◽  
pp. 413-425 ◽  
Author(s):  
Athanasios Alexiou ◽  
Stylianos Chatzichronis ◽  
Asma Perveen ◽  
Abdul Hafeez ◽  
Ghulam Md. Ashraf
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Review Paper ◽  
Boolean Networks ◽  
Diagnostic Tools ◽  
Complex Nature ◽  
Cellular Interactions ◽  
Protein Protein Interactions ◽  
Deterministic Models

Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

PlncRNADB: A Repository of Plant lncRNAs and lncRNA-RBP Protein Interactions

2019 ◽  
Vol 14 (7) ◽  
pp. 621-627 ◽  
Author(s):  
Youhuang Bai ◽  
Xiaozhuan Dai ◽  
Tiantian Ye ◽  
Peijing Zhang ◽  
Xu Yan ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Populus Trichocarpa ◽  
Noncoding Rnas ◽  
Reference Database ◽  
Protein Coding ◽  
Arabidopsis Lyrata ◽  
User Friendly

Background: Long noncoding RNAs (lncRNAs) are endogenous noncoding RNAs, arbitrarily longer than 200 nucleotides, that play critical roles in diverse biological processes. LncRNAs exist in different genomes ranging from animals to plants. Objective: PlncRNADB is a searchable database of lncRNA sequences and annotation in plants. Methods: We built a pipeline for lncRNA prediction in plants, providing a convenient utility for users to quickly distinguish potential noncoding RNAs from protein-coding transcripts. Results: More than five thousand lncRNAs are collected from four plant species (Arabidopsis thaliana, Arabidopsis lyrata, Populus trichocarpa and Zea mays) in PlncRNADB. Moreover, our database provides the relationship between lncRNAs and various RNA-binding proteins (RBPs), which can be displayed through a user-friendly web interface. Conclusion: PlncRNADB can serve as a reference database to investigate the lncRNAs and their interaction with RNA-binding proteins in plants. The PlncRNADB is freely available at http://bis.zju.edu.cn/PlncRNADB/.

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