Weighted Protein-protein Interaction Network Research Articles

ICDM-GEHC: identifying cancer driver module based on graph embedding and hierarchical clustering

Due to the high heterogeneity of cancers, it is rather essential to explore driver modules with the help of gene mutation data as well as known interactions between genes/proteins. Unfortunately, latent false positive interactions are inevitable in the Protein-Protein Interaction (PPI) network. Hence in the presented method, a new weight evaluation index, based on the gene-microRNA network as well as somatic mutation profile, is introduced for weighting the PPI network first. Subsequently, the vertices in the weighted PPI network are hierarchically clustered by measuring the Mahalanobis distance of their feature vectors, extracted with the graph embedding method Node2vec. Finally, a heuristic process with dropping and extracting is conducted on the gene clusters to produce a group of gene modules. Numerous experiment results demonstrate that the proposed method exhibits superior performance to four cutting-edge identification methods in most cases regarding the capability of recognizing the acknowledged cancer-related genes, generating modules having relatively high coverage and mutual exclusivity, and are significantly enriched for specific types of cancers. The majority of the genes in the identified modules are involved in cancer-related signaling pathways, or have been reported to be carcinogenic in the literature. Furthermore, many cancer related genes detected by the proposed method are actually omitted by the four comparison methods, which has been verified in the experiments.

CACO: A core-attachment method with cross-species functional ortholog information to detect human protein complexes.

Protein complexes play an essential role in living cells. Detecting protein complexes is crucial to understand protein functions and treat complex diseases. Due to high time and resource consumption of experiment approaches, many computational approaches have been proposed to detect protein complexes. However, most of them are only based on protein-protein interaction (PPI) networks, which heavily suffer from the noise in PPI networks. Therefore, we propose a novel core-attachment method, named CACO, to detect human protein complexes, by integrating the functional information from other species via protein ortholog relations. First, CACO constructs a cross-species ortholog relation matrix and transfers GO terms from other species as a reference to evaluate the confidence of PPIs. Then, a PPI filter strategy is adopted to clean the PPI network and thus a weighted clean PPI network is constructed. Finally, a new effective core-attachment algorithm is proposed to detect protein complexes from the weighted PPI network. Compared to other thirteen state-of-the-art methods, CACO outperforms all of them in terms of F-measure and Composite Score, showing that integrating ortholog information and the proposed core-attachment algorithm are effective in detecting protein complexes. CACO is available at https://csuligroup.com/CACO/ and https://github.com/CSUBioGroup/CACO.

Detecting protein complexes with multiple properties by an adaptive harmony search algorithm

BackgroundAccurate identification of protein complexes in protein-protein interaction (PPI) networks is crucial for understanding the principles of cellular organization. Most computational methods ignore the fact that proteins in a protein complex have a functional similarity and are co-localized and co-expressed at the same place and time, respectively. Meanwhile, the parameters of the current methods are specified by users, so these methods cannot effectively deal with different input PPI networks.ResultTo address these issues, this study proposes a new method called MP-AHSA to detect protein complexes with Multiple Properties (MP), and an Adaptation Harmony Search Algorithm is developed to optimize the parameters of the MP algorithm. First, a weighted PPI network is constructed using functional annotations, and multiple biological properties and the Markov cluster algorithm (MCL) are used to mine protein complex cores. Then, a fitness function is defined, and a protein complex forming strategy is designed to detect attachment proteins and form protein complexes. Next, a protein complex filtering strategy is formulated to filter out the protein complexes. Finally, an adaptation harmony search algorithm is developed to determine the MP algorithm’s parameters automatically.ConclusionsExperimental results show that the proposed MP-AHSA method outperforms 14 state-of-the-art methods for identifying protein complexes. Also, the functional enrichment analyses reveal that the protein complexes identified by the MP-AHSA algorithm have significant biological relevance.

A supervised protein complex prediction method with network representation learning and gene ontology knowledge

BackgroundProtein complexes are essential for biologists to understand cell organization and function effectively. In recent years, predicting complexes from protein–protein interaction (PPI) networks through computational methods is one of the current research hotspots. Many methods for protein complex prediction have been proposed. However, how to use the information of known protein complexes is still a fundamental problem that needs to be solved urgently in predicting protein complexes.ResultsTo solve these problems, we propose a supervised learning method based on network representation learning and gene ontology knowledge, which can fully use the information of known protein complexes to predict new protein complexes. This method first constructs a weighted PPI network based on gene ontology knowledge and topology information, reducing the network's noise problem. On this basis, the topological information of known protein complexes is extracted as features, and the supervised learning model SVCC is obtained according to the feature training. At the same time, the SVCC model is used to predict candidate protein complexes from the protein interaction network. Then, we use the network representation learning method to obtain the vector representation of the protein complex and train the random forest model. Finally, we use the random forest model to classify the candidate protein complexes to obtain the final predicted protein complexes. We evaluate the performance of the proposed method on two publicly PPI data sets.ConclusionsExperimental results show that our method can effectively improve the performance of protein complex recognition compared with existing methods. In addition, we also analyze the biological significance of protein complexes predicted by our method and other methods. The results show that the protein complexes predicted by our method have high biological significance.

Network-Based Approaches for Disease-Gene Association Prediction Using Protein-Protein Interaction Networks

Genome-wide association studies (GWAS) can be used to infer genome intervals that are involved in genetic diseases. However, investigating a large number of putative mutations for GWAS is resource- and time-intensive. Network-based computational approaches are being used for efficient disease-gene association prediction. Network-based methods are based on the underlying assumption that the genes causing the same diseases are located close to each other in a molecular network, such as a protein-protein interaction (PPI) network. In this survey, we provide an overview of network-based disease-gene association prediction methods based on three categories: graph-theoretic algorithms, machine learning algorithms, and an integration of these two. We experimented with six selected methods to compare their prediction performance using a heterogeneous network constructed by combining a genome-wide weighted PPI network, an ontology-based disease network, and disease-gene associations. The experiment was conducted in two different settings according to the presence and absence of known disease-associated genes. The results revealed that HerGePred, an integrative method, outperformed in the presence of known disease-associated genes, whereas PRINCE, which adopted a network propagation algorithm, was the most competitive in the absence of known disease-associated genes. Overall, the results demonstrated that the integrative methods performed better than the methods using graph-theory only, and the methods using a heterogeneous network performed better than those using a homogeneous PPI network only.

An Ensemble Learning Framework for Detecting Protein Complexes From PPI Networks.

Detecting protein complexes is one of the keys to understanding cellular organization and processes principles. With high-throughput experiments and computing science development, it has become possible to detect protein complexes by computational methods. However, most computational methods are based on either unsupervised learning or supervised learning. Unsupervised learning-based methods do not need training datasets, but they can only detect one or several topological protein complexes. Supervised learning-based methods can detect protein complexes with different topological structures. However, they are usually based on a type of training model, and the generalization of a single model is poor. Therefore, we propose an Ensemble Learning Framework for Detecting Protein Complexes (ELF-DPC) within protein-protein interaction (PPI) networks to address these challenges. The ELF-DPC first constructs the weighted PPI network by combining topological and biological information. Second, it mines protein complex cores using the protein complex core mining strategy we designed. Third, it obtains an ensemble learning model by integrating structural modularity and a trained voting regressor model. Finally, it extends the protein complex cores and forms protein complexes by a graph heuristic search strategy. The experimental results demonstrate that ELF-DPC performs better than the twelve state-of-the-art approaches. Moreover, functional enrichment analysis illustrated that ELF-DPC could detect biologically meaningful protein complexes. The code/dataset is available for free download from https://github.com/RongquanWang/ELF-DPC.

An Iterative Method for Predicting Essential Proteins Based on Multifeature Fusion and Linear Neighborhood Similarity.

Growing evidence have demonstrated that many biological processes are inseparable from the participation of key proteins. In this paper, a novel iterative method called linear neighborhood similarity-based protein multifeatures fusion (LNSPF) is proposed to identify potential key proteins based on multifeature fusion. In LNSPF, an original protein-protein interaction (PPI) network will be constructed first based on known protein-protein interaction data downloaded from benchmark databases, based on which, topological features will be further extracted. Next, gene expression data of proteins will be adopted to transfer the original PPI network to a weighted PPI network based on the linear neighborhood similarity. After that, subcellular localization and homologous information of proteins will be integrated to extract functional features for proteins, and based on both functional and topological features obtained above. And then, an iterative method will be designed and carried out to predict potential key proteins. At last, for evaluating the predictive performance of LNSPF, extensive experiments have been done, and compare results between LNPSF and 15 state-of-the-art competitive methods have demonstrated that LNSPF can achieve satisfactory recognition accuracy, which is markedly better than that achieved by each competing method.

An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks.

Identifying the protein complexes in protein-protein interaction (PPI) networks is essential for understanding cellular organization and biological processes. To address the high false positive/negative rates of PPI networks and detect protein complexes with multiple topological structures, we developed a novel improved memetic algorithm (IMA). IMA first combines the topological and biological properties to obtain a weighted PPI network with reduced noise. Next, it integrates various clustering results to construct the initial populations. Furthermore, a fitness function is designed based on the five topological properties of the protein complexes. Finally, we describe the rest of our IMA method, which primarily consists of four steps: selection operator, recombination operator, local optimization strategy, and updating the population operator. In particular, IMA is a combination of genetic algorithm and a local optimization strategy, which has a strong global search ability, and searches for local optimal solutions effectively. The experimental results demonstrate that IMA performs much better than the base methods and existing state-of-the-art techniques. The source code and datasets of the IMA can be found at https://github.com/RongquanWang/IMA.

A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization.

Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein–protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein–protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein–protein interaction networks.

A Novel Model for Identifying Essential Proteins Based on Key Target Convergence Sets.

In recent years, many computational models have been designed to detect essential proteins based on protein-protein interaction (PPI) networks. However, due to the incompleteness of PPI networks, the prediction accuracy of these models is still not satisfactory. In this manuscript, a novel key target convergence sets based prediction model (KTCSPM) is proposed to identify essential proteins. In KTCSPM, a weighted PPI network and a weighted (Domain-Domain Interaction) network are constructed first based on known PPIs and PDIs downloaded from benchmark databases. And then, by integrating these two kinds of networks, a novel weighted PDI network is built. Next, through assigning a unique key target convergence set (KTCS) for each node in the weighted PDI network, an improved method based on the random walk with restart is designed to identify essential proteins. Finally, in order to evaluate the predictive effects of KTCSPM, it is compared with 12 competitive state-of-the-art models, and experimental results show that KTCSPM can achieve better prediction accuracy. Considering the satisfactory predictive performance achieved by KTCSPM, it indicates that KTCSPM might be a good supplement to the future research on prediction of essential proteins.

Essential Protein Prediction Based on node2vec and XGBoost.

Essential proteins are a vital part of the survival of organisms and cells. Identification of essential proteins lays a solid foundation for understanding protein functions and discovering drug targets. The traditional biological experiments are expensive and time-consuming. Recently, many computational methods have been proposed. However, some noises in the protein-protein interaction (PPI) networks affect the efficiency of essential protein prediction. It is necessary to construct a credible PPI network by using other useful biological information to reduce the effects of these noises. In this article, we proposed a model, Ess-NEXG, to identify essential proteins, which integrates biological information, including orthologous information, subcellular localization information, RNA-Seq information, and PPI network. In our model, first, we constructed a credible weighted PPI network by using different types of biological information. Second, we extracted the topological features of proteins in the constructed weighted PPI network by using the node2vec technique. Last, we used eXtreme Gradient Boosting (XGBoost) to predict essential proteins by using the topological features of proteins. The extensive results show that our model has better performance than other computational methods.

Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network.

In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.

DPCMNE: Detecting Protein Complexes From Protein-Protein Interaction Networks Via Multi-Level Network Embedding.

Biological functions of a cell are typically carried out through protein complexes. The detection of protein complexes is therefore of great significance for understanding the cellular organizations and protein functions. In the past decades, many computational methods have been proposed to detect protein complexes. However, most of the existing methods just search the local topological information to mine dense subgraphs as protein complexes, ignoring the global topological information. To tackle this issue, we propose the DPCMNE method to detect protein complexes via multi-level network embedding. It can preserve both the local and global topological information of biological networks. First, DPCMNE employs a hierarchical compressing strategy to recursively compress the input protein-protein interaction (PPI) network into multi-level smaller PPI networks. Then, a network embedding method is applied on these smaller PPI networks to learn protein embeddings of different levels of granularity. The embeddings learned from all the compressed PPI networks are concatenated to represent the final protein embeddings of the original input PPI network. Finally, a core-attachment based strategy is adopted to detect protein complexes in the weighted PPI network constructed by the pairwise similarity of protein embeddings. To assess the efficiency of our proposed method, DPCMNE is compared with other eight clustering algorithms on two yeast datasets. The experimental results show that the performance of DPCMNE outperforms those state-of-the-art complex detection methods in terms of F1 and F1+Acc. Furthermore, the results of functional enrichment analysis indicate that protein complexes detected by DPCMNE are more biologically significant in terms of P-score.

A novel graph clustering method with a greedy heuristic search algorithm for mining protein complexes from dynamic and static PPI networks

Discovering protein complexes from protein-protein interaction (PPI) networks is one of the primary tasks in bioinformatics. However, most of the state-of-the-art methods still face some challenges, such as the inability to discover overlapping protein complexes, failure to consider the inherent structure of real protein complexes, and non-utilization of biological information. Based on the above mentioned aspects, we present a novel graph clustering method with a greedy heuristic search algorithm for mining protein complexes using a new clustering model in dynamic and static weighted PPI networks (named MPC-C). First, MPC-C constructed dynamic and static weighted PPI networks by combining biological and topological information. Second, initial clusters were obtained using core and multifunctional proteins, following which we proposed a greedy heuristic search algorithm to expand each initial cluster and form candidate protein complexes in dynamic and static weighted PPI networks. Finally, unreliable and highly overlapping protein complexes were discarded. To demonstrate the performance of MPC-C, we tested this method on five PPI networks and compared it with nine other effective methods. The experimental results indicate that MPC-C outperformed the other state-of-the-art methods with respect to various computational and biologically relevant metrics.

IdenPC-MIIP: identify protein complexes from weighted PPI networks using mutual important interacting partner relation.

Protein complexes are key units for studying a cell system. During the past decades, the genome-scale protein-protein interaction (PPI) data have been determined by high-throughput approaches, which enables the identification of protein complexes from PPI networks. However, the high-throughput approaches often produce considerable fraction of false positive and negative samples. In this study, we propose the mutual important interacting partner relation to reflect the co-complex relationship of two proteins based on their interaction neighborhoods. In addition, a new algorithm called idenPC-MIIP is developed to identify protein complexes from weighted PPI networks. The experimental results on two widely used datasets show that idenPC-MIIP outperforms 17 state-of-the-art methods, especially for identification of small protein complexes with only two or three proteins.

An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins

Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the formation of PPI networks. Up to now, a series of computational methods for predicting essential proteins from PPI networks have been proposed successively. However, the prediction accuracy of these computational methods is still not quite satisfactory. In this paper, a novel prediction method called CVIM is proposed to infer potential essential proteins. In CVIM, original PPI networks will be first transferred into weighted PPI networks by implementing PCC (Pearson Correlation Coefficient) on protein gene expression data. And then, based on weighted PPI networks and information of orthologous proteins, some critical network topological features and protein functional features will be extracted for each protein in the weighted PPI network. Finally, based on these newly extracted topological and functional features of proteins, an iterative algorithm will be designed to predict essential proteins. In order to evaluate the identification performance of CVIM, we have compared CVIM with 13 kinds of state-of-the-art prediction methods. Experimental results show that CVIM can achieve prediction accuracies of 92%, 80% and 71% out of the top 1%, 5% and 10% candidate proteins separately, which significantly outperform the prediction accuracies achieved by those state-of-the-art prediction methods. We have demonstrated that the prediction accuracy of essential proteins can be effectively improved by integrating the functional and network topological characteristics of proteins, which means that the novel method CVIM may be an excellent addition to the protein researches in the future.

A Novel Model for Predicting Essential Proteins Based on Heterogeneous Protein-Domain Network

Essential proteins play significant roles in cell survive. In current years, some Protein-Protein Interaction (PPI) data have been discovered in saccharomyces cerevisiae. Due to the high costs of biological experiments, a growing number of computational models are adopted to predict essential proteins. However, the identification accuracy of these computational models still has broad space for improvement. In this paper, a novel prediction model called NPRI is proposed to infer potential essential proteins based on the PageRank algorithm. In NPRI, a new heterogeneous Protein-Domain network will be constructed by integrating three kinds of networks such as the weighted PPI network, the Domain-Domain network and the initial Protein-Domain network first. Here, these three kinds of networks are established in accordance with gene expression data, original PPI network and known Protein-Domain network respectively. Next, based on the newly constructed heterogeneous Protein-Domain network, we will extract functional features and topological characteristics for each protein to further construct a novel distribution rate network. And then, an improved iteration method based on the PageRank algorithm will be implemented on the novel distribution rate network to infer essential proteins. Finally, in order to evaluate the performance of NPRI, we will compare NPRI with other state-of-the-art prediction models, and simulation results show that NPRI can achieve reliable identification accuracies of 90%, 84.5% and 79% in top 100, 200 and 300 predicted candidate essential proteins separately, which outperform these competitive models remarkably, and means that NPRI is a promising framework for identifying essential proteins as well.

An Iterative Method for Identifying Essential Proteins Based on Non-Negative Matrix Factorization

In recent years, with the development of high-throughput technologies, lots of computational methods for predicting essential proteins based on protein-protein interaction (PPI) networks and biological information of proteins have been proposed successively. However, due to the incompleteness of PPI networks, the prediction accuracy achieved by these methods is still unsatisfactory, and it remains to be a challenging work to design effective computational models to identify essential proteins. In this manuscript, a novel Prediction Model based on the Non-negative Matrix Factorization (PMNMF for abbreviation) is proposed. In PMNMF, an original PPI network will be constructed first based on PPIs downloaded from any given benchmark database. And then, based on topological features of protein nodes, the original PPI network will be further converted to a weighted PPI network. Moreover, in order to overcome the incompleteness of PPI networks, the NMF (Non-negative Matrix Factorization) method will be implemented on the weighted PPI network to obtain a transition probability matrix. And then, by integrating biological information including the gene expression information, homologous information and subcellular localization information of proteins, a unique initial score will be calculated and assigned to each protein node in the weighed PPI network, based on which, an improved Page-Rank algorithm will be designed to infer potential essential proteins. Finally, in order to evaluate the performance of PMNMF, it will be compared with 14 state-of-the-art prediction models, and experimental results show that PMNMF can achieve the best identification accuracy.

A novel extended Pareto Optimality Consensus model for predicting essential proteins

Essential proteins have vital functions, when they are destroyed in cells, the cells will die or stop reproducing. Therefore, it is very important to identify essential proteins from a large number of other proteins. Due to the time-consuming, expensive, and inefficient process in biological experimental methods, computational methods become more and more popular to recognize them. In the early stages, these methods mainly rely on protein-protein interaction (PPI) information, which limits their discovery capacities. Researchers find novel methods by fusing multi-information to improve prediction accuracy. According to these features, essential proteins are more important and conservative in the evolution process, their neighbors in PPI networks are usually likely to be essential, there are many false positives in PPI data, whether a protein is essential can be assessed by the importance of a protein itself, the relevance of neighbors and the reliability of PPIs. The importance of neighbors and the reliability of PPIs can be further integrated into neighborhood feature. In the study, orthologous information, edge-clustering coefficient and gene expression information are used to measure the importance of a protein itself, the importance of the neighbors and the reliability of PPIs, respectively. Then, a novel expanded POC model, E_POC, is proposed to fuse the above information to discover essential proteins, a weighted PPI network is constructed. The proteins ranked high according to their weights are treated as candidate essential proteins. This novel method is named as E_POC. E_POC outperforms the existing classical methods on S. cerevisiae and E. coli data.

Protein complex identification based on weighted PPI network with multi-source information

Proteins form complexes to accomplish biological functions such as transcription of DNA, translation of mRNA and cell growth. Detection of protein complexes from protein-protein interaction (PPI) networks is the first step for the analysis of biological processes and pathways. Here, we propose a new framework by incorporating Gene Ontology (GO), amino acid background frequency (AABF) and data from von Mering (von Mering data) to identify protein complexes. Firstly, based on the semantic similarity of GO, we construct a weighted PPI network. Secondly, von Mering data is added to construct six types of weighted graphs. Lastly, by integrating density, diameter and cosine similarity, we define a new condition for clustering proteins in these weighted protein network by selecting specific node as key node. Comparison and analysis results indicate that our proposed method could achieve better performances than some classic existing approaches in regard to f-measure and precision.