Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters

Hanna, Eileen Marie; Zaki, Nazar; Amin, Amr

doi:10.1371/journal.pone.0144163

Cited by 32 publications

(14 citation statements)

References 57 publications

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“…Next, we compared our method with DyCluster [ 25 ] in the Tables 10 , 11 , 12 , 13 , 14 and 15 . DyCluster is a framework to detect complexes based on PPI data and gene expression data, which was proposed by Hanna et al .…”

Section: Resultsmentioning

confidence: 99%

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Construction of dynamic probabilistic protein interaction networks for protein complex identification

et al. 2016

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BackgroundRecently, high-throughput experimental techniques have generated a large amount of protein-protein interaction (PPI) data which can construct large complex PPI networks for numerous organisms. System biology attempts to understand cellular organization and function by analyzing these PPI networks. However, most studies still focus on static PPI networks which neglect the dynamic information of PPI.ResultsThe gene expression data under different time points and conditions can reveal the dynamic information of proteins. In this study, we used an active probability-based method to distinguish the active level of proteins at different active time points. We constructed dynamic probabilistic protein networks (DPPN) to integrate dynamic information of protein into static PPI networks. Based on DPPN, we subsequently proposed a novel method to identify protein complexes, which could effectively exploit topological structure as well as dynamic information of DPPN. We used three different yeast PPI datasets and gene expression data to construct three DPPNs. When applied to three DPPNs, many well-characterized protein complexes were accurately identified by this method.ConclusionThe shift from static PPI networks to dynamic PPI networks is essential to accurately identify protein complex. This method not only can be applied to identify protein complex, but also establish a framework to integrate dynamic information into static networks for other applications, such as pathway analysis.

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Section: Resultsmentioning

confidence: 99%

“…Recently, Hanna et al . proposed a framework termed DyCluster to detect complexes based on PPI networks and gene expression data [ 25 ]. Firstly, DyCluster uses biclustering techniques to model the dynamic aspect of PPI networks by incorporating gene expression data.…”

Section: Introductionmentioning

confidence: 99%

Construction of dynamic probabilistic protein interaction networks for protein complex identification

et al. 2016

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“…It is showed that using a biclustering algorithm to extract some dynamic subnetworks from static PPI networks, improve the accuracy of protein complex detection methods[ 64 ]. To have a better view of the proposed genetic-based algorithm advantages, in this subsection, we compare our proposed biclustering method, GA-DCT, with some popular biclustering algorithms CC[ 39 ], BiMax[ 44 ], xMOTISs[ 46 ], OPSM[ 47 ],ISA[ 48 ] and UniBic[ 65 ].…”

Section: Resultsmentioning

confidence: 99%

BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes

Lakizadeh

Jalili

2016

PLoS ONE

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Considering the roles of protein complexes in many biological processes in the cell, detection of protein complexes from available protein-protein interaction (PPI) networks is a key challenge in the post genome era. Despite high dynamicity of cellular systems and dynamic interaction between proteins in a cell, most computational methods have focused on static networks which cannot represent the inherent dynamicity of protein interactions. Recently, some researchers try to exploit the dynamicity of PPI networks by constructing a set of dynamic PPI subnetworks correspondent to each time-point (column) in a gene expression data. However, many genes can participate in multiple biological processes and cellular processes are not necessarily related to every sample, but they might be relevant only for a subset of samples. So, it is more interesting to explore each subnetwork based on a subset of genes and conditions (i.e., biclusters) in a gene expression data. Here, we present a new method, called BiCAMWI to employ dynamicity in detecting protein complexes. The preprocessing phase of the proposed method is based on a novel genetic algorithm that extracts some sets of genes that are co-regulated under some conditions from input gene expression data. Each extracted gene set is called bicluster. In the detection phase of the proposed method, then, based on the biclusters, some dynamic PPI subnetworks are extracted from input static PPI network. Protein complexes are identified by applying a detection method on each dynamic PPI subnetwork and aggregating the results. Experimental results confirm that BiCAMWI effectively models the dynamicity inherent in static PPI networks and achieves significantly better results than state-of-the-art methods. So, we suggest BiCAMWI as a more reliable method for protein complex detection.

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“…Shi et al [25] introduced a semi-supervised learning method based on neural network that uses biological and topological feature vectors. The DyCluster [26] method utilized the gene expression data for detection of a protein complex. A number of computational approaches along with their frameworks and feature sets are depicted in Table 1.…”

Section: Introductionmentioning

confidence: 99%

IoMT-Based Association Rule Mining for the Prediction of Human Protein Complexes

et al. 2020

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The inspiring increase in the Internet-enabling devices has influenced health industry due to the nature of these devices where they offer health related information swiftly. One of the prominent characteristics of these devices is to provide physicians with effective diagnosis of sensitive diseases. Internet of Medical Things (IoMT) is a means of connecting medical devices to computing nodes with the help of Internet for affording real-time communications between patients and clinicians to understand the interaction of human protein complexes. A secure and correct protein complex prediction plays an important job in perceiving the principal method of various cellular determinations and to elucidate the functionality of different un-annotated proteins. Different experimental schemes have been evolved to accomplish this task, however, these schemes have high error rates and are not efficient in terms of time, cost, privacy, and security. To tackle these limitations, numerous computational models have been developed that consider a protein complex as a dense sub-graph and utilize some basic topological properties such as density and degree statistics as a feature set for protein complex prediction. Different kinds of sub-graph structures, e.g., ring, star, linear, and hybrid have also been found in Protein-Protein Interaction Network (PPIN), therefore, more advance topological properties may be helpful to predict these structures. Moreover, the amino acid sequence of protein determines its formation, thus, the sequence information is important for predicting the interacting property among proteins in a secure way. In this study, we have computed basic as well as advance topological features by considering the interaction network of human protein complexes in the IoMT environment. In addition, biological features, i.e., discrete wavelet coefficients, length, and entropy from amino acid sequences of proteins have been computed. The supervised learning method based on association rules such as Partial Tree (PART) and Non-Nested Generalized Exemplars (NNGE) are trained to identify human protein complexes on the basis of integrated topological and biological properties. The 10-fold cross validation is exercised to measure the proposed methods. Experimental results show that association rule learners with integrated features outperform other complex mining algorithms, i.e., probabilistic Bayesian Network (BN), and Random Forest, in terms of accuracy and efficiency in addition to provide privacy.

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Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters

Cited by 32 publications

References 57 publications

Construction of dynamic probabilistic protein interaction networks for protein complex identification

Construction of dynamic probabilistic protein interaction networks for protein complex identification

BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes

IoMT-Based Association Rule Mining for the Prediction of Human Protein Complexes

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