DISTANCE-WISE PATHWAY DISCOVERY FROM PROTEIN–PROTEIN INTERACTION NETWORKS WEIGHTED BY SEMANTIC SIMILARITY

2014 ◽  
Vol 12 (01) ◽  
pp. 1450004 ◽  
Author(s):  
SLAVKA JAROMERSKA ◽  
PETR PRAUS ◽  
YOUNG-RAE CHO
Keyword(s):  
Signaling Pathways ◽  
Semantic Similarity ◽  
Protein Interaction ◽  
Protein Pair ◽  
Semantic Analysis ◽  
Topological Analysis ◽  
Cellular Mechanisms ◽  
Semantic Similarity Measure ◽  
Protein Protein Interaction ◽  
Ppi Networks

Reconstruction of signaling pathways is crucial for understanding cellular mechanisms. A pathway is represented as a path of a signaling cascade involving a series of proteins to perform a particular function. Since a protein pair involved in signaling and response have a strong interaction, putative pathways can be detected from protein–protein interaction (PPI) networks. However, predicting directed pathways from the undirected genome-wide PPI networks has been challenging. We present a novel computational algorithm to efficiently predict signaling pathways from PPI networks given a starting protein and an ending protein. Our approach integrates topological analysis of PPI networks and semantic analysis of PPIs using Gene Ontology data. An advanced semantic similarity measure is used for weighting each interacting protein pair. Our distance-wise algorithm iteratively selects an adjacent protein from a PPI network to build a pathway based on a distance condition. On each iteration, the strength of a hypothetical path passing through a candidate edge is estimated by a local heuristic. We evaluate the performance by comparing the resultant paths to known signaling pathways on yeast. The results show that our approach has higher accuracy and efficiency than previous methods.

scholarly journals A Non-negative Matrix Factorization Based Method for Identifying Essential Proteins

2021 ◽  
Author(s):  
Zhihong Zhang ◽  
Sai Hu ◽  
Wei Yan ◽  
Bihai Zhao ◽  
Lei Wang
Keyword(s):  
Protein Interaction ◽  
Matrix Factorization ◽  
Biological Data ◽  
Protein Domain ◽  
Biological Information ◽  
Ppi Network ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Non Negative Matrix Factorization

Abstract BackgroundIdentification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, various different computational methods have been proposed to identify essential proteins based on protein-protein interaction (PPI) networks. However, there has been reliable evidence that a huge amount of false negatives and false positives exist in PPI data. Therefore, it is necessary to reduce the influence of false data on accuracy of essential proteins prediction by integrating multi-source biological information with PPI networks.ResultsIn this paper, we proposed a non-negative matrix factorization and multiple biological information based model (NDM) for identifying essential proteins. The first stage in this progress was to construct a weighted PPI network by combing the information of protein domain, protein complex and the topology characteristic of the original PPI network. Then, the non-negative matrix factorization technique was used to reconstruct an optimized PPI network with whole enough weight of edges. In the final stage, the ranking score of each protein was computed by the PageRank algorithm in which the initial scores were calculated with homologous and subcellular localization information. In order to verify the effectiveness of the NDM method, we compared the NDM with other state-of-the-art essential proteins prediction methods. The comparison of the results obtained from different methods indicated that our NDM model has better performance in predicting essential proteins.ConclusionEmploying the non-negative matrix factorization and integrating multi-source biological data can effectively improve quality of the PPI network, which resulted in the led to optimization of the performance essential proteins identification. This will also provide a new perspective for other prediction based on protein-protein interaction networks.

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1969
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1969 ◽  
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available in the STRING database, we use a network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

2015 ◽  
Vol 4 (4) ◽  
pp. 35-51 ◽  
Author(s):  
Bandana Barman ◽  
Anirban Mukhopadhyay
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Wild Type ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Hiv 1

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

scholarly journals Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

2018 ◽  
Vol 35 (14) ◽  
pp. 2395-2402 ◽  
Author(s):  
Xiaoying Wang ◽  
Bin Yu ◽  
Anjun Ma ◽  
Cheng Chen ◽  
Bingqiang Liu ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Protein Interaction ◽  
Feature Selection Method ◽  
Feature Space ◽  
Supplementary Information ◽  
Sequence Profile ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Imbalance Problem ◽  
Ppi Networks

Abstract Motivation The prediction of protein–protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples. Results A new ensemble learning-based method, Ensemble Learning of synthetic minority oversampling technique (SMOTE) for Unbalancing samples and RF algorithm (EL-SMURF), was proposed for PPI sites prediction in this study. The sequence profile feature and the residue evolution rates were combined for feature extraction of neighboring residues using a sliding window, and the SMOTE was applied to oversample interface residues in the feature space for the imbalance problem. The Multi-dimensional Scaling feature selection method was implemented to reduce feature redundancy and subset selection. Finally, the Random Forest classifiers were applied to build the ensemble learning model, and the optimal feature vectors were inserted into EL-SMURF to predict PPI sites. The performance validation of EL-SMURF on two independent validation datasets showed 77.1% and 77.7% accuracy, which were 6.2–15.7% and 6.1–18.9% higher than the other existing tools, respectively. Availability and implementation The source codes and data used in this study are publicly available at http://github.com/QUST-AIBBDRC/EL-SMURF/. Supplementary information Supplementary data are available at Bioinformatics online.

Topological Analysis and Sub-Network Mining of Protein-Protein Interactions

2007 ◽  
pp. 209-240 ◽  
Author(s):  
Daniel Wu ◽  
Xiaohua Hu
Keyword(s):  
Power Law ◽  
Protein Interaction ◽  
Topological Analysis ◽  
Interaction Network ◽  
Data Sets ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Confidence Levels ◽  
Experimental Systems ◽  
Average Vertex

In this chapter, we report a comprehensive evaluation of the topological structure of protein-protein interaction (PPI) networks, by mining and analyzing graphs constructed from the popular data sets publicly available to the bioinformatics research community. We compare the topology of these networks across different species, different confidence levels, and different experimental systems used to obtain the interaction data. Our results confirm the well-accepted claim that the degree distribution follows a power law. However, further statistical analysis shows that residues are not independent on the fit values, indicating that the power law model may be inadequate. Our results also show that the dependence of the average clustering coefficient on the vertices degree is far from a power law, contradicting many published results. For the first time, we report that the average vertex density exhibits a strong powder law dependence on the vertices degree for the networks studied, regardless of species, confidence levels, and experimental systems. We also present an efficient and accurate approach to detecting a community in a protein-protein interaction network from a given seed protein. Our experimental results show strong structural and functional relationships among member proteins within each of the communities identified by our approach, as verified by MIPS complex catalog database and annotations.

A Pathway and Network Oriented Approach to Enlighten Molecular Mechanisms of Type 2 Diabetes Using Multiple Association Studies

2020 ◽  
Author(s):  
Burcu Bakir-Gungor ◽  
Miray Unlu Yazici ◽  
Gokhan Goy ◽  
Mustafa Temiz
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Protein Interaction ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Oriented Approach

AbstractDiabetes Mellitus (DM) is a group of metabolic disorder that is characterized by pancreatic dysfunction in insulin producing beta cells, glucagon secreting alpha cells, and insulin resistance or insulin in-functionality related hyperglycemia. Type 2 Diabetes Mellitus (T2D), which constitutes 90% of the diabetes cases, is a complex multifactorial disease. In the last decade, genome-wide association studies (GWASs) for type 2 diabetes (T2D) successfully pinpointed the genetic variants (typically single nucleotide polymorphisms, SNPs) that associate with disease risk. However, traditional GWASs focus on the ‘the tip of the iceberg’ SNPs, and the SNPs with mild effects are discarded. In order to diminish the burden of multiple testing in GWAS, researchers attempted to evaluate the collective effects of interesting variants. In this regard, pathway-based analyses of GWAS became popular to discover novel multi-genic functional associations. Still, to reveal the unaccounted 85 to 90% of T2D variation, which lies hidden in GWAS datasets, new post-GWAS strategies need to be developed. In this respect, here we reanalyze three meta-analysis data of GWAS in T2D, using the methodology that we have developed to identify disease-associated pathways by combining nominally significant evidence of genetic association with the known biochemical pathways, protein-protein interaction (PPI) networks, and the functional information of selected SNPs. In this research effort, to enlighten the molecular mechanisms underlying T2D development and progress, we integrated different in-silico approaches that proceed in top-down manner and bottom-up manner, and hence presented a comprehensive analysis at protein subnetwork, pathway, and pathway subnetwork levels. Our network and pathway-oriented approach is based on both the significance level of an affected pathway and its topological relationship with its neighbor pathways. Using the mutual information based on the shared genes, the identified protein subnetworks and the affected pathways of each dataset were compared. While, most of the identified pathways recapitulate the pathophysiology of T2D, our results show that incorporating SNP functional properties, protein-protein interaction networks into GWAS can dissect leading molecular pathways, which cannot be picked up using traditional analyses. We hope to bridge the knowledge gap from sequence to consequence.

Nonessential-Nonhub Proteins in the Protein-Protein Interaction Network

2014 ◽  
Vol 934 ◽  
pp. 159-164
Author(s):  
Yun Yuan Dong ◽  
Xian Chun Zhang
Keyword(s):  
Protein Interaction ◽  
Interaction Network ◽  
Clustering Coefficient ◽  
Centrality Measures ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Comparison Results ◽  
A Cell ◽  
High Degree

Protein-protein interaction (PPI) networks provide a simplified overview of the web of interactions that take place inside a cell. According to the centrality-lethality rule, hub proteins (proteins with high degree) tend to be essential in the PPI network. Moreover, there are also many low degree proteins in the PPI network, but they have different lethality. Some of them are essential proteins (essential-nonhub proteins), and the others are not (nonessential-nonhub proteins). In order to explain why nonessential-nonhub proteins don’t have essentiality, we propose a new measure n-iep (the number of essential neighbors) and compare nonessential-nonhub proteins with essential-nonhub proteins from topological, evolutionary and functional view. The comparison results show that there are statistical differences between nonessential-nonhub proteins and essential-nonhub proteins in centrality measures, clustering coefficient, evolutionary rate and the number of essential neighbors. These are reasons why nonessential-nonhub proteins don’t have lethality.

Sign in / Sign up

Export Citation Format

Share Document