scholarly journals Protein complexes detection based on node local properties and gene expression in PPI weighted networks

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Yang Yu ◽  
Dezhou Kong
Keyword(s):  
Gene Expression ◽  
Resource Allocation ◽  
Protein Complexes ◽  
Second Order ◽  
Protein Protein Interaction ◽  
Protein Complex Detection ◽  
Ppi Networks ◽  
Weight Protein ◽  
Local Properties ◽  
Effective Use

Abstract Background Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed. Most algorithms usually employ direct neighbors of nodes and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection. Result Based on this observation, we propose a new way by combining node resource allocation and gene expression information to weight protein network (NRAGE-WPN), in which protein complexes are detected based on core-attachment and second-order neighbors. Conclusions Through comparison with eleven methods in Yeast and Human PPI network, the experimental results demonstrate that this algorithm not only performs better than other methods on 75% in terms of f-measure+, but also can achieve an ideal overall performance in terms of a composite score consisting of five performance measures. This identification method is simple and can accurately identify more complexes.

scholarly journals Protein Complexes Detection Based on Node Local Properties and Gene Expression On PPI Weighted Networks

2021 ◽  
Author(s):  
Yang Yu ◽  
Dezhou Kong
Keyword(s):  
Gene Expression ◽  
Resource Allocation ◽  
Protein Complex ◽  
Protein Complexes ◽  
Second Order ◽  
Topological Properties ◽  
Ppi Network ◽  
Weighted Networks ◽  
Gene Expression Information ◽  
Local Properties

Abstract Background Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed to solve this issue. These algorithms usually consider a node’s direct neighbors and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection.Results To overcome this deficiency, this paper proposes a new protein complex identification method based on node-local topological properties and gene expression information on a new weighted PPI network, named NLPGE-WPN (joint node-local topological properties and gene expression information on weighted PPI network). First, based on the resource allocation of the PPI network and gene expression, a new weight metric is designed to describe the interaction between proteins. Second, our method constructs a series of dense complex cores based on density and network diameter constraints; the final complexes are recognized by expanding the second-order neighbor nodes of core complexes. Experimental results demonstrate that this algorithm has improved the performances of precision and f-measure, which is more valid in identifying protein complexes．Conclusions This identification method is simple and can accurately identify more complexes by integrating node-local properties and gene expression on PPI weighted networks.

scholarly journals Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jie Zhao ◽  
Xiujuan Lei ◽  
Fang-Xiang Wu
Keyword(s):  
Protein Complex ◽  
Protein Complexes ◽  
Critical Role ◽  
Cuckoo Search ◽  
Cellular Mechanisms ◽  
Protein Protein Interaction ◽  
Detection Algorithms ◽  
Protein Complex Detection ◽  
Ppi Networks ◽  
Complex Detection

Protein complexes play a critical role in understanding the biological processes and the functions of cellular mechanisms. Most existing protein complex detection algorithms cannot reflect dynamics of protein complexes. In this paper, a novel algorithm named Improved Cuckoo Search Clustering (ICSC) algorithm is proposed to detect protein complexes in weighted dynamic protein-protein interaction (PPI) networks. First, we constructed weighted dynamic PPI networks and detected protein complex cores in each dynamic subnetwork. Then, ICSC algorithm was used to cluster the protein attachments to the cores. The experimental results on both DIP dataset and Krogan dataset demonstrated that ICSC algorithm is more effective in identifying protein complexes than other competing methods.

scholarly journals Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Min Li ◽  
Weijie Chen ◽  
Jianxin Wang ◽  
Fang-Xiang Wu ◽  
Yi Pan
Keyword(s):  
Gene Expression ◽  
Computational Methods ◽  
Protein Complex ◽  
Protein Complexes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Protein Protein Interaction ◽  
Cellular Life ◽  
Ppi Networks ◽  
Core Proteins

Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data ofSaccharomyces cerevisiaeand the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures.

scholarly journals Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Jinxiong Zhang ◽  
Cheng Zhong ◽  
Hai Xiang Lin ◽  
Mian Wang
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Protein Complexes ◽  
Biological Data ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Temporal Interval ◽  
Protein Protein Interaction ◽  
Identification Method ◽  
Ppi Networks

Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods.

scholarly journals NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

2019 ◽  
Vol 47 (W1) ◽  
pp. W234-W241 ◽  
Author(s):  
Guangyan Zhou ◽  
Othman Soufan ◽  
Jessica Ewald ◽  
Robert E W Hancock ◽  
Niladri Basu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Visual Analytics ◽  
Regulatory Networks ◽  
Meta Analysis ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Perform Gene Expression ◽  
And Performance

Abstract The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

A METHOD BASED ON LOCAL DENSITY AND RANDOM WALKS FOR COMPLEXES DETECTION IN PROTEIN INTERACTION NETWORKS

2010 ◽  
Vol 08 (supp01) ◽  
pp. 47-62 ◽  
Author(s):  
LIANG YU ◽  
LIN GAO ◽  
KUI LI
Keyword(s):  
Random Walks ◽  
Protein Interaction ◽  
Local Density ◽  
Protein Complexes ◽  
Biological Significance ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Comprehensive Comparison ◽  
Attachment Proteins ◽  
Two Stages

In this paper, we present a method based on local density and random walks (LDRW) for core-attachment complexes detection in protein-protein interaction (PPI) networks whether they are weighted or not. Our LDRW method consists of two stages. Firstly, it finds all the protein-complex cores based on local density of subnetwork. Then it uses random walks with restarts for finding the attachment proteins of each detected core to form complexes. We evaluate the effectiveness of our method using two different yeast PPI networks and validate the biological significance of the predicted protein complexes using known complexes in the Munich Information Center for Protein Sequence (MIPS) and Gene Ontology (GO) databases. We also perform a comprehensive comparison between our method and other existing methods. The results show that our method can find more protein complexes with high biological significance and obtains a significant improvement. Furthermore, our method is able to identify biologically significant overlapped protein complexes.

scholarly journals Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia

2019 ◽  
Author(s):  
Robersy Sanchez ◽  
Sally A. Mackenzie
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Cytosine Methylation ◽  
Complex Disease ◽  
Lymphoblastic Leukemia ◽  
Integrated Analysis ◽  
Ras Signaling ◽  
Protein Protein Interaction ◽  
Diagnosis And Prognosis ◽  
Ppi Networks

AbstractGenome-wide DNA methylation and gene expression are commonly altered in pediatric acute lymphoblastic leukemia (PALL). Integrated analysis of cytosine methylation and expression datasets has the potential to provide deeper insights into the complex disease states and their causes than individual disconnected analyses. Studies of protein-protein interaction (PPI) networks of differentially methylated (DMGs) and expressed genes (DEGs) showed that gene expression and methylation consistently targeted the same gene pathways associated with cancer: Pathways in cancer, Ras signaling pathway, PI3K-Akt signaling pathway, and Rap1 signaling pathway, among others. Detected gene hubs and hub sub-networks are integrated by signature loci associated with cancer that include, for example, NOTCH1, RAC1, PIK3CD, BCL2, and EGFR. Statistical analysis disclosed a stochastic deterministic dependence between methylation and gene expression within the set of genes simultaneously identified as DEGs and DMGs, where larger values of gene expression changes are probabilistically associated with larger values of methylation changes. Concordance analysis of the overlap between enriched pathways in DEG and DMG datasets revealed statistically significant agreement between gene expression and methylation changes, reflecting a coordinated response of methylation and gene-expression regulatory systems. These results support the identification of reliable and stable biomarkers for cancer diagnosis and prognosis.

Denoising Protein–Protein interaction network via variational graph auto-encoder for protein complex detection

2020 ◽  
Vol 18 (03) ◽  
pp. 2040010 ◽  
Author(s):  
Heng Yao ◽  
Jihong Guan ◽  
Tianying Liu
Keyword(s):  
Protein Interaction ◽  
Protein Complex ◽  
Protein Complexes ◽  
Empirical Evaluation ◽  
High Rate ◽  
Detection Methods ◽  
Convolutional Network ◽  
Protein Protein Interaction ◽  
Protein Complex Detection ◽  
Complex Detection

Identifying protein complexes is an important issue in computational biology, as it benefits the understanding of cellular functions and the design of drugs. In the past decades, many computational methods have been proposed by mining dense subgraphs in Protein–Protein Interaction Networks (PINs). However, the high rate of false positive/negative interactions in PINs prevents accurately detecting complexes directly from the raw PINs. In this paper, we propose a denoising approach for protein complex detection by using variational graph auto-encoder. First, we embed a PIN to vector space by a stacked graph convolutional network (GCN), then decide which interactions in the PIN are credible. If the probability of an interaction being credible is less than a threshold, we delete the interaction. In such a way, we reconstruct a reliable PIN. Following that, we detect protein complexes in the reconstructed PIN by using several typical detection methods, including CPM, Coach, DPClus, GraphEntropy, IPCA and MCODE, and compare the results with those obtained directly from the original PIN. We conduct the empirical evaluation on four yeast PPI datasets (Gavin, Krogan, DIP and Wiphi) and two human PPI datasets (Reactome and Reactomekb), against two yeast complex benchmarks (CYC2008 and MIPS) and three human complex benchmarks (REACT, REACT_uniprotkb and CORE_COMPLEX_human), respectively. Experimental results show that with the reconstructed PINs obtained by our denoising approach, complex detection performance can get obviously boosted, in most cases by over 5%, sometimes even by 200%. Furthermore, we compare our approach with two existing denoising methods (RWS and RedNemo) while varying different matching rates on separate complex distributions. Our results show that in most cases (over 2/3), the proposed approach outperforms the existing methods.

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