A new method for predicting essential proteins based on participation degree in protein complex and subgraph density

Xiujuan Lei; Xiaoqin Yang

doi:10.1371/journal.pone.0198998

Abstract

Essential proteins are crucial to living cells. Identification of essential proteins from protein-protein interaction (PPI) networks can be applied to pathway analysis and function prediction, furthermore, it can contribute to disease diagnosis and drug design. There have been some experimental and computational methods designed to identify essential proteins, however, the prediction precision remains to be improved. In this paper, we propose a new method for identifying essential proteins based on Participation degree of a protein in protein Complexes and Subgraph Density, named as PCSD. In order to test the performance of PCSD, four PPI datasets (DIP, Krogan, MIPS and Gavin) are used to conduct experiments. The experiment results have demonstrated that PCSD achieves a better performance for predicting essential proteins compared with some competing methods including DC, SC, EC, IC, LAC, NC, WDC, PeC, UDoNC, and compared with the most recent method LBCC, PCSD can correctly predict more essential proteins from certain numbers of top ranked proteins on the DIP dataset, which indicates that PCSD is very effective in discovering essential proteins in most case.

Citation: Lei X, Yang X (2018) A new method for predicting essential proteins based on participation degree in protein complex and subgraph density. PLoS ONE 13(6): e0198998. https://doi.org/10.1371/journal.pone.0198998

Editor: Gideon Schreiber, Weizmann Institute of Science, ISRAEL

Received: March 24, 2018; Accepted: May 30, 2018; Published: June 12, 2018

Copyright: © 2018 Lei, Yang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This paper is supported by the National Natural Science Foundation of China (61672334, 61502290, 61401263). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Proteins are the products of genes, and they are the vital material and functional units for living organisms. Essential proteins are those proteins which are indispensable for organisms to normally grow and multiply. Thus accurately identifying essential proteins makes important contribution to understanding the key biological processes of an organism at molecular level, which is beneficial to guide disease diagnosis and drug design.

In the previous studies, both experimental and computational approaches have been exploited to detect essential proteins. The experimental approaches for identifying essential proteins, such as single gene knockout [1], RNA interference [2] and conditional knockout [3], all of which are time consuming and expensive. Consequently, a large number of computational approaches are developed to identify essential proteins with the support of large-scale PPI data gained by utilizing high-throughput techniques. Initially, computational approaches mainly focused on the topological properties of biological networks, and there are a series of topological centrality measures following the “centrality-lethality” principle. Among these centrality measures, Degree Centrality (DC) [4], Betweeness Centrality (BC) [5], Closeness Centrality (CC) [6], Eigenvector Centrality (EC) [7], Information Centrality (IC) [8] and Subgraph Centrality (SC) [9] are the classical ones. In addition, some other effective centrality measures, i.e., maximum neighborhood component (MNC) and density of maximum neighborhood component (DMNC) [10], Local Average Connectivity (LAC) [11], Neighborhood Centrality (NC) [12], local interaction density (LID) [13], TP and TP-NC [14] have been also designed to identify essential proteins. CytoNCA [15], a plugin of Cytoscape for centrality analysis and evaluation of biological networks, has been developed to conveniently predict essential proteins. However, all these topological centrality measures ignore the intrinsic biological characteristics of essential proteins and there are a lot of false positives and false negatives in PPI networks, thus the identification accuracies of essential proteins were affected. To overcome these limitations, many researchers attempt to combine network topology and biology information.

Based on the combination of gene expression profiles and PPI data, Li et al. proposed an approach named PeC [16] and Tang et al. proposed a modified one named WDC [17]. By analyzing the correlation between proteins and their domain features, Peng et al. proposed a new prediction method, named UDoNC, by combining the domain features of proteins with their topological properties in PPI network [18]. Peng et al. proposed another method named ION [19] by integrating the orthology with PPI networks, which is based on random walk model. Based on sub-network partition and prioritization by integrating subcellular localization information, Li et al. proposed a new network-based essential protein prediction method, named SPP [20]. Moreover, some researchers exploit protein complexes information to predict essential proteins. For example, Luo et al. proposed LIDC for predicting essential proteins by combing local interaction density with in-degree centrality of complexes [21]. Qin et al. proposed LBCC, which is based on the combination of local density, betweenness centrality (BC) and in-degree centrality of complex (IDC) [22]. Li et al. proposed UC to identify essential proteins by integrating protein complexes with topological features of PPI networks [23]. In addition, to diminish the impacts of inherent false negatives and false positives in PPI data, Li et al. purified the PPI network by integrating gene expressions and subcellular localizations to construct a reliable network [24] [25], and Chen et al. constructed integrated dynamic PPI networks by employing RNA-Seq datasets [26]. There is a detailed introduction about essential proteins discovery methods based on the PPI networks in [27].

In this study, based on the integration of participation degree in protein complexes and subgraph density, a new centrality measure method PCSD is proposed. First of all, refined PPI networks (RPINs) are constructed by applying gene expressions. We calculate the participation degree in complexes for each protein based on the weighted RPINs generated by Edge Clustering Coefficient (ECC) and Pearson Correlation Coefficient (PCC). We construct a subgraph for each protein, which is compose of the protein as well as its direct (level 1) and indirect (level 2) neighbors, and weight the interactions in the subgraph based on sharing GO annotations (SG) and sharing protein complexes (SC), then the subgraph density is measured. Finally, a linear combination model is used to integrate two parts of score. The experiment results show that the proposed method PCSD outperforms other existing methods, such as DC, SC, EC, IC, LAC, NC, WDC, PeC, UDoNC, and so on.

The remainder of the paper is organized as follows. Section 2 describes the PCSD algorithm in details. Section 3 presents the computational experiment results and analysis, and Section 4 concludes the paper.

Methods

Refined PPI network construction

It is well known that the protein interactions are changing over time, environments and different stages of cell cycle [28], thus the original PPI networks cannot accurately reflect the real protein interactions in cell. In this study, we construct relatively reliable PPI networks by utilizing time-course gene expression data according to three-sigma principle [28]. The three-sigma principle is used to determine the active threshold for each protein based on the characteristics of its expression curve. For a time point, a gene is considered to be expressed if its corresponding gene expression value is greater than or equal to its active threshold. Two proteins should have higher possibility to physically interact with each other if their corresponding genes are both expressed at the same time point [24], in this case, the two proteins are also called as co-expressed protein pairs. We delete those PPIs whose two corresponding proteins are not co-expressed at any time point from original PPI networks. Consequently, a refined PPI network (RPIN) can be constructed.

Participation degree in protein complexes

In this section, we will analyze the essentiality of proteins in terms of participation degree of proteins in complexes. At first, the RPINs need to be weighted. Previous studies show that both the Edge Clustering Coefficient (ECC) and Pearson Correlation Coefficient (PCC) are effective ways to weight PPIs [29] [30], which measure the degree of closeness of physical interactions and the strength of co-expression between two proteins, respectively. Therefore, our method PCSD weights RPINs by integrating ECC (see Eq (1)) and PCC (see Eq (2)). The Edge Clustering Coefficient (ECC) between protein v_i and v_j is defined as [31]: (1) where Z_ij is the number of triangles the edge (v_i, v_j) actually participates in, d_i and d_j denote the degree of protein v_i and v_j, respectively. The Pearson Correlation Coefficient (PCC) between protein v_i and v_j is defined as: (2) where x = {x₁, x₂,…, x_n} and y = {y₁, y₂,…, y_n} give the gene expression values of protein v_i and v_j at n time points, and represent the mean of gene expression value of x and y, respectively. The PCC values range from -1 to 1, for convenience, this study replaces PCC_ij by (PCC_ij+1)/2. By integrating PCC and ECC, the probability that two proteins interact with each other can be described from the perspective of network topology and gene expression, therefore, the importance of the interaction between protein v_i and v_j is defined as follows: (3) And the weighted degree (sum of weights, SW) of protein v_i is defined as: (4) where N(v_i) is the neighbors set of protein v_i.

Protein complexes are stable macromolecular assemblies that play a key role in diverse biochemical activities. [23] suggested that it is more possible to be essential for the proteins included in complexes than those not included in any complexes and the proteins appeared in multiple complexes are more inclined to be essential compared with those only appeared in a single complex. In our design, we calculate the participation degree of a protein in complexes to help characterizing the essentiality of proteins. Proteins participating in complexes includes direct participation and indirect participation. If a protein is included in complexes, that is to say, the protein directly participate in complexes. And if a protein isn’t included in any complexes, but its some neighbors appear in complexes, in this case, the protein indirectly participate in complexes. Otherwise, the protein doesn’t participate in complexes. The Participation degrees in Complexes (PC) of protein v_i is defined as (5) where V(|C|) represents all the proteins which are contained in some complexes, C_i represents the protein complexes which contain protein v_i and SW_in(v_i, C_i) denotes weighted degree of protein v_i in the complex C_i.

Subgraph density

In this section, we assess the essentiality of proteins by considering local properties of proteins in a PPI network, and construct a subgraph for each protein within the second order of neighbors. By doing this, the new technique can measure topological information in a larger area. Owing to the small-world property of the majority of biological networks, an index related to higher order neighbors may involve too many nodes, which is not efficient for detecting the essentiality of nodes [26]. Thus, we think that within the second order of neighbors is enough. Previous researches on protein complex detection [32] and essential protein prediction [33] suggest that the performance of the prediction algorithm based on weighted networks is superior to that based on un-weighted networks. Therefore, to calculate the subgraph density, we weight the PPIs between protein pairs in subgraphs by applying GO annotations and protein complexes information. If there are some sharing GO annotations between two interacting proteins, the two proteins have the same function, and the interaction between them becomes strong [30]. We define SG_ij to describe the relationship (see Eq (6)). Similarly, if two interacting proteins are contained in a common complex, the interaction between proteins becomes more reliable. We define SC_ij to describe the relationship (see Eq (7)). (6) (7) where |G_i| and |G_j| denote the number of GO annotations for protein v_i and v_j, respectively. |G_i ∩ G_j| denotes the number of sharing GO annotations for both protein v_i and protein v_j. |C_i| and |C_j| denote the number of protein complexes containing protein v_i and v_j, respectively. |C_i ∩ C_j| denotes the number of sharing protein complexes annotating both protein v_i and protein v_j. Finally, the Subgraph Density (SD) of v_i within its second order of neighbors is defined as follows. (8) where Ns denotes the number of the proteins contained in a subgraph.

Essential protein prediction method PCSD

Our method PCSD can rank all proteins in RPINs according to their computed scores. The final essentiality scores is determined by two components: one is the participation degree in complexes PC scores obtained in 2.2 section, the other is the subgraph density SD scores obtained in 2.3 section. A linear combination model is used to integrate PC and SD score. For a given protein v_i, its essentiality is evaluated by PCSD(v_i): (9) where α is a parameter to adjust the contributions of PC and SD. When α = 0, only the subgraph density is considered, and when α = 1, only the participation degree in complexes is considered. We will discuss the value of α in detail in Experiments and Results section.

Results and discussion

Experimental data

In order to evaluate the performance of proposed method PCSD, we conduct a group of experiments on Saccharomyces cerevisiae protein data. Four sets of PPI network data were used, including DIP [34], Krogan [35], MIPS [36], Gavin [37]. DIP PPIs were downloaded from (http://dip.mbi.ucla.edu/dip/). MIPS PPIs were downloaded from (ftp://ftpmips.gsf.de/fungi/Saccharomycetes/CYGD/). The PPIs data of Krogan and Gavin come from BioGRID database version 3.4.142 [38]. All self-interactions and repeated interactions were removed as a data preprocessing of these PPIs. The details of all these four PPIs are presented in Table 1. The known essential proteins data were collected from four different databases: MIPS [39], SGD [40], DEG [41] and SGDP [42]. Gene expression data were obtained from GEO (Gene Expression Omnibus) [43] with accession number GSE3431. It contains 9336 genes at 36 time points in 3 cell metabolism cycles. Proteins with gene expression data cover 96.98% of proteins in the DIP data, 98.88% of proteins in the Krogan data, 97.80% of proteins in the MIPS data and 99.16% of proteins in the Gavin data. The GO data we used in this study are cut-down version of the GO ontologies [44], which is available at (http://www.yeastgenome.org/download-data/curation). 745 protein complexes were collected from four protein complex datasets: CM270 [39], CM425 [45], CYC408 and CYC428 [46] [47], which covered 2167 proteins in total.

Download:

Table 1. The detail information of the four PPI datasets.

https://doi.org/10.1371/journal.pone.0198998.t001

Comparison with other methods

In this section, we compare PCSD with other essential proteins prediction methods (DC, SC, EC, IC, LAC, NC, WDC, PeC, UDoNC and LBCC) using the four datasets described in the Experimental data section. As UDoNC needs protein domain data, for convenience, UDoNC is only applied on DIP PPI network as mentioned in their paper [18]. And LBCC is applied on DIP and MIPS datasets as mentioned in their paper [22]. First, proteins are ranked in descending order according to their scores calculated by each method. Then, the top 1, 5, 10, 15, 20, 25 percent of all proteins are selected as candidate essential proteins, and finally, the number of true essential proteins in these essential protein candidates is determined according to gold standard dataset of known essential proteins. We visualize the proportion of essential proteins in top ranked proteins for all methods. The comparative results are shown in Figs 1–4. The method PCSD was conducted on four refined PPI networks and the other methods were conducted on original PPI networks.

Download:

Fig 1. The number of true essential proteins predicted by PCSD and other several methods on DIP dataset.

https://doi.org/10.1371/journal.pone.0198998.g001

Download:

Fig 2. The number of true essential proteins predicted by PCSD and other several methods on Krogan dataset.

https://doi.org/10.1371/journal.pone.0198998.g002

Download:

Fig 3. The number of true essential proteins predicted by PCSD and other several methods on MIPS dataset.

https://doi.org/10.1371/journal.pone.0198998.g003

Download:

Fig 4. The number of true essential proteins predicted by PCSD and other several methods on Gavin dataset.

https://doi.org/10.1371/journal.pone.0198998.g004

For the DIP dataset shown in Fig 1, PCSD outperforms all the other ten methods from top 1% to 15% of ranked proteins, and LBCC has the best performance at top 20% and top 25%. Let us take the top 1% as an example, 45 essential proteins are correctly predicted by PCSD while 22, 24, 24, 24, 29, 32, 36, 39, 37and 37 for DC, SC, EC, IC, LAC, NC, WDC, PeC, UDoNC and LBCC, respectively.

For the Krogan dataset shown in Fig 2, PCSD achieves the best performance compared with other eight methods from top 1% to top 25% of ranked proteins. Let us take the top 25% of ranked proteins as an example, 351 essential proteins are correctly predicted by PCSD while 318, 272, 253, 314, 325, 323, 332 and 317 for DC, SC, EC, IC, LAC, NC, WDC and PeC, respectively.

For the MIPS dataset shown in Fig 3, LBCC obtains the best results from top 1% and 25% of ranked proteins. Except for LBCC, the performance of PCSD is obviously superior to that of the other eight methods at various proportions of top ranked proteins. And for the other eight compared methods, the largest number of true essential proteins identified are 16(PeC), 93(PeC), 185(PeC), 248(PeC), 312(WDC) and 374(WDC) at six percentages from top 1% to top 25%. By comparison, PCSD correctly predicted 33, 151, 272, 357, 426 and 475 essential proteins, and achieved more than 106, 62, 47, 43, 36 and 27 percent improvements, respectively.

For the Gavin dataset shown in Fig 4, compared with other eight methods, PCSD can identify more essential proteins at the 5%, 10%, 15%, 20% and 25% of top ranked proteins. At top 1% level, LAC and PeC correctly identified all 14 true essential proteins, the number of true essential proteins identified by PCSD is 12, which is near to the result obtained by LAC and PeC.

Thus, experiment results stated above indicate that PCSD can more effectively predict essential proteins than the other methods in most cases.

Validation with jackknife methodology

In this section, we employ the jackknife methodology to evaluate furtherly the performance of PCSD as well as other identification methods. The results are shown in Figs 5–8. The horizontal axis of the jackknife curves represents the proteins ranked based on scores of essentiality calculated by each method in descending order from left to right. We chose the top 1000 proteins for each dataset to analyze the performance of PCSD and other methods. The vertical axis of the jackknife curves represents the number of true essential proteins among the top N proteins, where N is the number along the horizontal axis. The Jackknife curve also reveal that our method PCSD has a better performance than other several methods.

Download:

Fig 5. The jackknife curves of PCSD and other several methods for the DIP dataset.

https://doi.org/10.1371/journal.pone.0198998.g005

Download:

Fig 6. The jackknife curves of PCSD and other several methods for the Krogan dataset.

https://doi.org/10.1371/journal.pone.0198998.g006

Download:

Fig 7. The jackknife curves of PCSD and other several methods for the MIPS dataset.

https://doi.org/10.1371/journal.pone.0198998.g007

Download:

Fig 8. The jackknife curves of PCSD and other several methods for the Gavin dataset.

https://doi.org/10.1371/journal.pone.0198998.g008

Validation with precision-recall curves

In addition, to assess the effectiveness of PCSD, we calculate the precision and recall of PCSD and other several methods, and plot the precision-recall cure for each method. Precision represents the proportion of predicted essential proteins that match the known ones. Recall represents the proportion of known essential proteins that are matched by predicted ones. They are defined as follows: (10) (11) where TP is the number of true positives, which denotes essential proteins correctly identified as essential, FP is the number of false positives, which denotes non-essential proteins incorrectly predicted as essential and FN is the number of false negatives, which denotes essential proteins incorrectly predicted as non-essential. The results are shown as Figs 9–12, from which we can observe that compared with other methods, the PR curve of the new proposed method has an improvement on predicting essential proteins for all the four different datasets.

Download:

Fig 9. The PR curves of PCSD and other several methods for the DIP dataset.

https://doi.org/10.1371/journal.pone.0198998.g009

Download:

Fig 10. The PR curves of PCSD and other several methods for the Krogan dataset.

https://doi.org/10.1371/journal.pone.0198998.g010

Download:

Fig 11. The PR curves of PCSD and other several methods for the MIPS dataset.

https://doi.org/10.1371/journal.pone.0198998.g011

Download:

Fig 12. The PR curves of PCSD and other several methods for the Gavin dataset.

https://doi.org/10.1371/journal.pone.0198998.g012

The analysis of refining PPI networks

In the PCSD method, to improve the prediction precision of essential proteins, refined PPI networks are constructed by deleting those unreliable protein-protein interactions in the first place. The numbers of edges of original and refined networks for four PPI datasets are shown in Table 2. In order to validate the effectiveness of refining PPI networks, we compare the prediction performance on original and refined PPI networks and plot The Receiver Operating Characteristics (ROC) curve, which is a good way of evaluating a classifier’s performance [48]. In an ROC curve, the horizontal axis represents the values of true positive rate (TPR) and vertical axis represents the values of false positive rate (FPR). They are defined as follows. (12) (13) where the means of TP, FP and FN are the same with the ones in Eqs (10) and (11), and TN is the number of true negatives, which denotes non-essential proteins correctly predicted as non-essential. The area under the ROC curves (AUC) is used to measure the performance of predicting essential proteins on original and refined PPI networks, the larger the AUC value is, the better the prediction performance is. The ROC curves for four PPI datasets are shown in Fig 13, from which we can observe that the values of AUC on refined PPI networks are always higher than those on original PPI networks for four different datasets. The AUC are 0.68461 and 0.69853 for original and refined DIP PPI network, respectively, and there is a little improvement. However, the prediction performance on refined PPI network is obviously better compared with that on original PPI network for Krogan, MIPS and Gavin datasets. Therefore, it is effective to improve the essential proteins identification precision by refining the original PPI networks.

Download:

Table 2. The number of edges for original and refined PPI networks.

https://doi.org/10.1371/journal.pone.0198998.t002

Download:

Fig 13. The ROC curves on original and refined PPI networks for (a) DIP dataset, (b) Kroan dataset, (c) MIPS dataset and (d) Gavin dataset.

https://doi.org/10.1371/journal.pone.0198998.g013

The analysis of parameter α

In our method PCSD, the ranking scores of proteins compose of two parts: participation degree in complexes and subgraph density, which are adjusted by parameter α. We set the value of α ranges from 0 to 1. When α is assigned as 0, 0.1, 0.2, … 0.9 and 1, respectively, the prediction results of PCSD are presented in Table 3. When α = 0, only the subgraph density is considered, and when α = 1, only the participation degree in complexes is considered. From Table 3, we can see that when the value of α ranges from 0.5 to 1, the performance of PCSD is better. Because the performance of PCSD has slight difference when predicting the top 15%, 20% and 25% of top ranked proteins, we set the value as 0.8 for α to conduct experiments on four datasets in this study.

Download:

Table 3. The number of true essential proteins correctly identified by PCSD with different α.

https://doi.org/10.1371/journal.pone.0198998.t003

Conclusions

Essential proteins play a crucial role in the viability and reproduction of living organisms, and the identification of essential proteins contribute to promoting the process of disease study and drug design. At present, there are many computational methods proposed to detect essential proteins. In our study, we have proposed a new essential proteins prediction method that integrates participation degree in protein complexes and subgraph density, named PCSD. First, we construct a refined PPI network (RPIN), then, we calculate the participation degree in complexes for each protein based on the weighted RPINs generated by Edge Clustering Coefficient (ECC) and Pearson Correlation Coefficient (PCC), which determines the topological properties and co-expression characteristics of proteins, respectively. In addition, we construct a subgraph for each protein within the second order of neighbors, and weight the interactions in the subgraph based on sharing GO annotations (SG) and sharing protein complexes (SC), then the subgraph density is measured. Experiment results have shown that the proposed PCSD method can make an improvement in predicting essential proteins. Furthermore, researches have suggested that there is a close relationship between essential proteins and causing disease gene, so we will focus on identifying and prioritizing disease-related genes by combing various data sources in future.

Supporting information

S1 Excel. Standard essential proteins data.

https://doi.org/10.1371/journal.pone.0198998.s001

(XLSX)

S2 Excel. Gene expression data.

https://doi.org/10.1371/journal.pone.0198998.s002

(XLSX)

S3 Excel. Protein complex data.

https://doi.org/10.1371/journal.pone.0198998.s003

(XLSX)

S4 Excel. GO annotation data.

https://doi.org/10.1371/journal.pone.0198998.s004

(XLSX)

S1 Text. Protein interaction data in original DIP dataset.

https://doi.org/10.1371/journal.pone.0198998.s005

(TXT)

S2 Text. Protein interaction data in original Krogan dataset.

https://doi.org/10.1371/journal.pone.0198998.s006

(TXT)

S3 Text. Protein interaction data in original MIPS dataset.

https://doi.org/10.1371/journal.pone.0198998.s007

(TXT)

S4 Text. Protein interaction data in original Gavin dataset.

https://doi.org/10.1371/journal.pone.0198998.s008

(TXT)

S5 Text. Protein interaction data in refined DIP dataset.

https://doi.org/10.1371/journal.pone.0198998.s009

(TXT)

S6 Text. Protein interaction data in refined Krogan dataset.

https://doi.org/10.1371/journal.pone.0198998.s010

(TXT)

S7 Text. Protein interaction data in refined MIPS dataset.

https://doi.org/10.1371/journal.pone.0198998.s011

(TXT)

S8 Text. Protein interaction data in refined Gavin dataset.

https://doi.org/10.1371/journal.pone.0198998.s012

(TXT)

Acknowledgments

We thank the editors and reviewers for valuable suggestions for our manuscript.

References

1. Giaever G, Chu AM, Ni L, Connelly C, Riles L, Véronneau S, et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature. 2002;418(6896):387. pmid:12140549
- View Article
- PubMed/NCBI
- Google Scholar
2. Cullen LM, Arndt GM. Genome-wide screening for gene function using RNAi in mammalian cells. Immunology & Cell Biology. 2005;83(3):217.
- View Article
- Google Scholar
3. Roemer T, Jiang B, Davison J, Ketela T, Veillette K, Breton A, et al. Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery. Molecular Microbiology. 2003;50(1):167–81. pmid:14507372
- View Article
- PubMed/NCBI
- Google Scholar
4. Vallabhajosyula RR, Chakravarti D, Lutfeali S, Ray A, Raval A. Identifying hubs in protein interaction networks. Plos One. 2009;4(4):e5344. pmid:19399170
- View Article
- PubMed/NCBI
- Google Scholar
5. Newman MEJ. A measure of betweenness centrality based on random walks. Social Networks. 2005;27(1):39–54.
- View Article
- Google Scholar
6. Wuchty S, Stadler PF. Centers of complex networks. Journal of Theoretical Biology. 2003;223(1):45–53. pmid:12782116
- View Article
- PubMed/NCBI
- Google Scholar
7. Bonacich P. Power and Centrality: A Family of Measures. American Journal of Sociology. 1987;92(5):1170–82.
- View Article
- Google Scholar
8. Stephenson K, Zelen M. Rethinking centrality: Methods and examples ☆. Social Networks. 1989;11(1):1–37.
- View Article
- Google Scholar
9. Estrada E, Rodríguez-Velázquez JA. Subgraph centrality in complex networks. Physical Review E Statistical Nonlinear & Soft Matter Physics. 2005;71(2):056103.
- View Article
- Google Scholar
10. Lin CY, Chin CH, Wu HH, Chen SH, Ho CW, Ko MT. Hubba: hub objects analyzer—a framework of interactome hubs identification for network biology. Nucleic Acids Research. 2008;36(Web Server issue):W438. pmid:18503085
- View Article
- PubMed/NCBI
- Google Scholar
11. Li M, Wang J, Chen X, Wang H, Pan Y. A local average connectivity-based method for identifying essential proteins from the network level. Computational Biology & Chemistry. 2011;35(3):143.
- View Article
- Google Scholar
12. Wang J, Li M, Wang H, Pan Y. Identification of Essential Proteins Based on Edge Clustering Coefficient. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2012;9(4):1070–80.
- View Article
- Google Scholar
13. Qi Y, Luo J. Prediction of Essential Proteins Based on Local Interaction Density: IEEE Computer Society Press; 2016. 1170–82 p.
14. Li M, Lu Y, Wang J, Wu FX, Pan Y. A topology potential-based method for identifying essential proteins from PPI networks. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2015;12(2):372.
- View Article
- Google Scholar
15. Tang Y, Li M, Wang J, Pan Y, Wu FX. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems. 2015;127:67–72. pmid:25451770
- View Article
- PubMed/NCBI
- Google Scholar
16. Li M, Zhang H, Wang JX, Pan Y. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data. Bmc Systems Biology. 2012;6(1):15.
- View Article
- Google Scholar
17. Tang X, Wang J, Zhong J, Pan Y. Predicting Essential Proteins Based on Weighted Degree Centrality. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2014;11(2):407–18.
- View Article
- Google Scholar
18. Peng W, Wang J, Cheng Y, Lu Y. UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2015;12(2):276–88.
- View Article
- Google Scholar
19. Wei P, Wang J, Wang W, Liu Q, Wu FX, Yi P. Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks. Bmc Systems Biology. 2012;6(1):1–17.
- View Article
- Google Scholar
20. Li M, Li W, Wu FX, Pan Y, Wang J. Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information. Journal of Theoretical Biology. 2018.
- View Article
- Google Scholar
21. Luo J, Qi Y. Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes. Plos One. 2015;10(6):e0131418. pmid:26125187
- View Article
- PubMed/NCBI
- Google Scholar
22. Qin C, Sun Y, Dong Y. A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes. Plos One. 2016;11(8):e0161042. pmid:27529423
- View Article
- PubMed/NCBI
- Google Scholar
23. Li M, Lu Y, Niu Z, Wu FX. United Complex Centrality for Identification of Essential Proteins from PPI Networks. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2017;14(2):370–80.
- View Article
- Google Scholar
24. Min L, Peng N, Chen X, Wang J, Wu F, Yi P. Construction of refined protein interaction network for predicting essential proteins. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2017;PP(99):1-.
- View Article
- Google Scholar
25. Li M, Niu Z, Chen X, Zhong P, Wu F, Pan Y. A Reliable Neighbor-Based Method for Identifying Essential Proteins by Integrating Gene Expressions, Orthology, and Subcellular Localization Information. Tsinghua Science & Technology. 2016;21(6):668–77.
- View Article
- Google Scholar
26. Shang X, Wang Y, Chen B. Identifying essential proteins based on dynamic protein-protein interaction networks and RNA-Seq datasets. Science China Information Sciences. 2016;59(7):1–11.
- View Article
- Google Scholar
27. Zhu L, Zhang J, He L, Wang J, Peng Z, Jian Z. Essential Discovery Methods based on the Protein-Protein Interaction Networks. American Journal of Biochemistry and Biotechnology. 2017;13(4):242–51.
- View Article
- Google Scholar
28. Wang J, Peng X, Li M, Pan Y. Construction and application of dynamic protein interaction network based on time course gene expression data. Proteomics. 2013;13(2):301. pmid:23225755
- View Article
- PubMed/NCBI
- Google Scholar
29. Lei X, Ding Y, Fujita H, Zhang A. Identification of dynamic protein complexes based on fruit fly optimization algorithm. Knowledge-Based Systems. 2016;105(C):270–7.
- View Article
- Google Scholar
30. Lei X, Zhang Y, Cheng S, Wu FX, Pedrycz W. Topology Potential Based Seed-growth Method to Identify Protein Complexes on Dynamic PPI Data. Information Sciences. 2017.
- View Article
- Google Scholar
31. Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D. Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(9):2658. pmid:14981240
- View Article
- PubMed/NCBI
- Google Scholar
32. Zhao J, Lei X, Wu FX. Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC. 2017;2017:1–11.
- View Article
- Google Scholar
33. Fan C, Lei X, editors. Genome-Wide Identification of Essential Proteins by Integrating RNA-seq, Subcellular Location and Complexes Information. International Conference on Intelligent Computing; 2017.
34. Xenarios I, Salwínski L, Duan XJ, Higney P, Kim SM, Eisenberg D. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Research. 2002;30(1):303. pmid:11752321
- View Article
- PubMed/NCBI
- Google Scholar
35. Krogan NJ, Cagney G, Yu H, Zhong G, Guo X, Ignatchenko A, et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature. 2006;440(7084):637–43. pmid:16554755
- View Article
- PubMed/NCBI
- Google Scholar
36. Güldener U, Münsterkötter M, Oesterheld M, Pagel P, Ruepp A, Mewes HW, et al. MPact: the MIPS protein interaction resource on yeast. Nucleic Acids Research. 2006;34(Database issue):436–41.
- View Article
- Google Scholar
37. Gavin AC, Aloy P, Grandi P, Krause R, Boesche M, Marzioch M, et al. Proteome survey reveals modularity of the yeast cell machinery. Nature. 2006;440(7084):631–6. pmid:16429126
- View Article
- PubMed/NCBI
- Google Scholar
38. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Research. 2006;34(Database issue):535–9.
- View Article
- Google Scholar
39. Mewes HW, Frishman D, Mayer KF, Münsterkötter M, Noubibou O, Pagel P, et al. MIPS: analysis and annotation of proteins from whole genomes in 2005. Nucleic Acids Research. 2006;34(Database issue):D169. pmid:16381839
- View Article
- PubMed/NCBI
- Google Scholar
40. Cherry J, Adler C, Ball C, Chervitz S, Dwight S, Hester E, et al. SGD: Saccharomyces Genome Database. Nucleic Acids Research. 1998;26(1):73–9. pmid:9399804
- View Article
- PubMed/NCBI
- Google Scholar
41. Zhang R, Lin Y. DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Research. 2009;37(Database issue):455–8.
- View Article
- Google Scholar
42. Winzeler EA, Shoemaker DD, Astromoff A, Liang H, Anderson K, Andre B, et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science. 1999;285(5429):901. pmid:10436161
- View Article
- PubMed/NCBI
- Google Scholar
43. Tu BP, Kudlicki A, Rowicka M, Mcknight SL. Logic of the Yeast Metabolic Cycle: Temporal Compartmentalization of Cellular Processes. Science. 2005;310(5751):1152. pmid:16254148
- View Article
- PubMed/NCBI
- Google Scholar
44. Zhang Y, Lin H, Yang Z, Wang J, Li Y, Xu B. Protein Complex Prediction in Large Ontology Attributed Protein-Protein Interaction Networks. IEEE/ACM Trans Comput Biol Bioinform. 2013;10(3):729–41. pmid:24091405
- View Article
- PubMed/NCBI
- Google Scholar
45. Friedel CC, Krumsiek J, Zimmer R, editors. Bootstrapping the interactome: unsupervised identification of protein complexes in yeast. International Conference on Research in Computational Molecular Biology; 2008.
46. Pu S, Vlasblom J, Emili A, Greenblatt J, Wodak SJ. Identifying functional modules in the physical interactome of Saccharomyces cerevisiae. Proteomics. 2007;7(6):944–60. pmid:17370254
- View Article
- PubMed/NCBI
- Google Scholar
47. Pu S, Wong J, Turner B, Cho E, Wodak SJ. Up-to-date catalogues of yeast protein complexes. Nucleic Acids Research. 2009;37(3):825–31. pmid:19095691
- View Article
- PubMed/NCBI
- Google Scholar
48. Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition. 1997;30(7):1145–59.
- View Article
- Google Scholar

[ref1] 1. Giaever G, Chu AM, Ni L, Connelly C, Riles L, Véronneau S, et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature. 2002;418(6896):387. pmid:12140549
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Cullen LM, Arndt GM. Genome-wide screening for gene function using RNAi in mammalian cells. Immunology & Cell Biology. 2005;83(3):217.
View Article
Google Scholar

[6] View Article

[7] Google Scholar

[ref3] 3. Roemer T, Jiang B, Davison J, Ketela T, Veillette K, Breton A, et al. Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery. Molecular Microbiology. 2003;50(1):167–81. pmid:14507372
View Article
PubMed/NCBI
Google Scholar

[9] View Article

[10] PubMed/NCBI

[11] Google Scholar

[ref4] 4. Vallabhajosyula RR, Chakravarti D, Lutfeali S, Ray A, Raval A. Identifying hubs in protein interaction networks. Plos One. 2009;4(4):e5344. pmid:19399170
View Article
PubMed/NCBI
Google Scholar

[13] View Article

[14] PubMed/NCBI

[15] Google Scholar

[ref5] 5. Newman MEJ. A measure of betweenness centrality based on random walks. Social Networks. 2005;27(1):39–54.
View Article
Google Scholar

[17] View Article

[18] Google Scholar

[ref6] 6. Wuchty S, Stadler PF. Centers of complex networks. Journal of Theoretical Biology. 2003;223(1):45–53. pmid:12782116
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref7] 7. Bonacich P. Power and Centrality: A Family of Measures. American Journal of Sociology. 1987;92(5):1170–82.
View Article
Google Scholar

[24] View Article

[25] Google Scholar

[ref8] 8. Stephenson K, Zelen M. Rethinking centrality: Methods and examples ☆. Social Networks. 1989;11(1):1–37.
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref9] 9. Estrada E, Rodríguez-Velázquez JA. Subgraph centrality in complex networks. Physical Review E Statistical Nonlinear & Soft Matter Physics. 2005;71(2):056103.
View Article
Google Scholar

[30] View Article

[31] Google Scholar

[ref10] 10. Lin CY, Chin CH, Wu HH, Chen SH, Ho CW, Ko MT. Hubba: hub objects analyzer—a framework of interactome hubs identification for network biology. Nucleic Acids Research. 2008;36(Web Server issue):W438. pmid:18503085
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref11] 11. Li M, Wang J, Chen X, Wang H, Pan Y. A local average connectivity-based method for identifying essential proteins from the network level. Computational Biology & Chemistry. 2011;35(3):143.
View Article
Google Scholar

[37] View Article

[38] Google Scholar

[ref12] 12. Wang J, Li M, Wang H, Pan Y. Identification of Essential Proteins Based on Edge Clustering Coefficient. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2012;9(4):1070–80.
View Article
Google Scholar

[40] View Article

[41] Google Scholar

[ref13] 13. Qi Y, Luo J. Prediction of Essential Proteins Based on Local Interaction Density: IEEE Computer Society Press; 2016. 1170–82 p.

[ref14] 14. Li M, Lu Y, Wang J, Wu FX, Pan Y. A topology potential-based method for identifying essential proteins from PPI networks. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2015;12(2):372.
View Article
Google Scholar

[44] View Article

[45] Google Scholar

[ref15] 15. Tang Y, Li M, Wang J, Pan Y, Wu FX. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems. 2015;127:67–72. pmid:25451770
View Article
PubMed/NCBI
Google Scholar

[47] View Article

[48] PubMed/NCBI

[49] Google Scholar

[ref16] 16. Li M, Zhang H, Wang JX, Pan Y. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data. Bmc Systems Biology. 2012;6(1):15.
View Article
Google Scholar

[51] View Article

[52] Google Scholar

[ref17] 17. Tang X, Wang J, Zhong J, Pan Y. Predicting Essential Proteins Based on Weighted Degree Centrality. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2014;11(2):407–18.
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref18] 18. Peng W, Wang J, Cheng Y, Lu Y. UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2015;12(2):276–88.
View Article
Google Scholar

[57] View Article

[58] Google Scholar

[ref19] 19. Wei P, Wang J, Wang W, Liu Q, Wu FX, Yi P. Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks. Bmc Systems Biology. 2012;6(1):1–17.
View Article
Google Scholar

[60] View Article

[61] Google Scholar

[ref20] 20. Li M, Li W, Wu FX, Pan Y, Wang J. Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information. Journal of Theoretical Biology. 2018.
View Article
Google Scholar

[63] View Article

[64] Google Scholar

[ref21] 21. Luo J, Qi Y. Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes. Plos One. 2015;10(6):e0131418. pmid:26125187
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref22] 22. Qin C, Sun Y, Dong Y. A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes. Plos One. 2016;11(8):e0161042. pmid:27529423
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref23] 23. Li M, Lu Y, Niu Z, Wu FX. United Complex Centrality for Identification of Essential Proteins from PPI Networks. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2017;14(2):370–80.
View Article
Google Scholar

[74] View Article

[75] Google Scholar

[ref24] 24. Min L, Peng N, Chen X, Wang J, Wu F, Yi P. Construction of refined protein interaction network for predicting essential proteins. IEEE/ACM Transactions on Computational Biology & Bioinformatics. 2017;PP(99):1-.
View Article
Google Scholar

[77] View Article

[78] Google Scholar

[ref25] 25. Li M, Niu Z, Chen X, Zhong P, Wu F, Pan Y. A Reliable Neighbor-Based Method for Identifying Essential Proteins by Integrating Gene Expressions, Orthology, and Subcellular Localization Information. Tsinghua Science & Technology. 2016;21(6):668–77.
View Article
Google Scholar

[80] View Article

[81] Google Scholar

[ref26] 26. Shang X, Wang Y, Chen B. Identifying essential proteins based on dynamic protein-protein interaction networks and RNA-Seq datasets. Science China Information Sciences. 2016;59(7):1–11.
View Article
Google Scholar

[83] View Article

[84] Google Scholar

[ref27] 27. Zhu L, Zhang J, He L, Wang J, Peng Z, Jian Z. Essential Discovery Methods based on the Protein-Protein Interaction Networks. American Journal of Biochemistry and Biotechnology. 2017;13(4):242–51.
View Article
Google Scholar

[86] View Article

[87] Google Scholar

[ref28] 28. Wang J, Peng X, Li M, Pan Y. Construction and application of dynamic protein interaction network based on time course gene expression data. Proteomics. 2013;13(2):301. pmid:23225755
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref29] 29. Lei X, Ding Y, Fujita H, Zhang A. Identification of dynamic protein complexes based on fruit fly optimization algorithm. Knowledge-Based Systems. 2016;105(C):270–7.
View Article
Google Scholar

[93] View Article

[94] Google Scholar

[ref30] 30. Lei X, Zhang Y, Cheng S, Wu FX, Pedrycz W. Topology Potential Based Seed-growth Method to Identify Protein Complexes on Dynamic PPI Data. Information Sciences. 2017.
View Article
Google Scholar

[96] View Article

[97] Google Scholar

[ref31] 31. Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D. Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(9):2658. pmid:14981240
View Article
PubMed/NCBI
Google Scholar

[99] View Article

[100] PubMed/NCBI

[101] Google Scholar

[ref32] 32. Zhao J, Lei X, Wu FX. Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC. 2017;2017:1–11.
View Article
Google Scholar

[103] View Article

[104] Google Scholar

[ref33] 33. Fan C, Lei X, editors. Genome-Wide Identification of Essential Proteins by Integrating RNA-seq, Subcellular Location and Complexes Information. International Conference on Intelligent Computing; 2017.

[ref34] 34. Xenarios I, Salwínski L, Duan XJ, Higney P, Kim SM, Eisenberg D. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Research. 2002;30(1):303. pmid:11752321
View Article
PubMed/NCBI
Google Scholar

[107] View Article

[108] PubMed/NCBI

[109] Google Scholar

[ref35] 35. Krogan NJ, Cagney G, Yu H, Zhong G, Guo X, Ignatchenko A, et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature. 2006;440(7084):637–43. pmid:16554755
View Article
PubMed/NCBI
Google Scholar

[111] View Article

[112] PubMed/NCBI

[113] Google Scholar

[ref36] 36. Güldener U, Münsterkötter M, Oesterheld M, Pagel P, Ruepp A, Mewes HW, et al. MPact: the MIPS protein interaction resource on yeast. Nucleic Acids Research. 2006;34(Database issue):436–41.
View Article
Google Scholar

[115] View Article

[116] Google Scholar

[ref37] 37. Gavin AC, Aloy P, Grandi P, Krause R, Boesche M, Marzioch M, et al. Proteome survey reveals modularity of the yeast cell machinery. Nature. 2006;440(7084):631–6. pmid:16429126
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref38] 38. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Research. 2006;34(Database issue):535–9.
View Article
Google Scholar

[122] View Article

[123] Google Scholar

[ref39] 39. Mewes HW, Frishman D, Mayer KF, Münsterkötter M, Noubibou O, Pagel P, et al. MIPS: analysis and annotation of proteins from whole genomes in 2005. Nucleic Acids Research. 2006;34(Database issue):D169. pmid:16381839
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref40] 40. Cherry J, Adler C, Ball C, Chervitz S, Dwight S, Hester E, et al. SGD: Saccharomyces Genome Database. Nucleic Acids Research. 1998;26(1):73–9. pmid:9399804
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref41] 41. Zhang R, Lin Y. DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Research. 2009;37(Database issue):455–8.
View Article
Google Scholar

[133] View Article

[134] Google Scholar

[ref42] 42. Winzeler EA, Shoemaker DD, Astromoff A, Liang H, Anderson K, Andre B, et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science. 1999;285(5429):901. pmid:10436161
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref43] 43. Tu BP, Kudlicki A, Rowicka M, Mcknight SL. Logic of the Yeast Metabolic Cycle: Temporal Compartmentalization of Cellular Processes. Science. 2005;310(5751):1152. pmid:16254148
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref44] 44. Zhang Y, Lin H, Yang Z, Wang J, Li Y, Xu B. Protein Complex Prediction in Large Ontology Attributed Protein-Protein Interaction Networks. IEEE/ACM Trans Comput Biol Bioinform. 2013;10(3):729–41. pmid:24091405
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref45] 45. Friedel CC, Krumsiek J, Zimmer R, editors. Bootstrapping the interactome: unsupervised identification of protein complexes in yeast. International Conference on Research in Computational Molecular Biology; 2008.

[ref46] 46. Pu S, Vlasblom J, Emili A, Greenblatt J, Wodak SJ. Identifying functional modules in the physical interactome of Saccharomyces cerevisiae. Proteomics. 2007;7(6):944–60. pmid:17370254
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref47] 47. Pu S, Wong J, Turner B, Cho E, Wodak SJ. Up-to-date catalogues of yeast protein complexes. Nucleic Acids Research. 2009;37(3):825–31. pmid:19095691
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref48] 48. Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition. 1997;30(7):1145–59.
View Article
Google Scholar

[157] View Article

[158] Google Scholar

Figures

Abstract

Introduction

Methods

Refined PPI network construction

Participation degree in protein complexes

Subgraph density

Essential protein prediction method PCSD

Results and discussion

Experimental data

Comparison with other methods

Validation with jackknife methodology

Validation with precision-recall curves

The analysis of refining PPI networks

The analysis of parameter α

Conclusions

Supporting information

S1 Excel. Standard essential proteins data.

S2 Excel. Gene expression data.

S3 Excel. Protein complex data.

S4 Excel. GO annotation data.

S1 Text. Protein interaction data in original DIP dataset.

S2 Text. Protein interaction data in original Krogan dataset.

S3 Text. Protein interaction data in original MIPS dataset.

S4 Text. Protein interaction data in original Gavin dataset.

S5 Text. Protein interaction data in refined DIP dataset.

S6 Text. Protein interaction data in refined Krogan dataset.

S7 Text. Protein interaction data in refined MIPS dataset.

S8 Text. Protein interaction data in refined Gavin dataset.

Acknowledgments

References