- Methodology article
- Open Access
Cluster-based assessment of protein-protein interaction confidence
© Kamburov et al.; licensee BioMed Central Ltd. 2012
- Received: 26 March 2012
- Accepted: 16 August 2012
- Published: 10 October 2012
Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity.
We propose a novel, network topology based interaction confidence assessment method called CAPPIC (cluster-based assessment of protein-protein interaction confidence). It exploits the network’s inherent modular architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters intrinsically and does not require any parameter input or reference sets for confidence scoring.
On the basis of five yeast and two human physical interactome maps inferred using different techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other approaches that are also based on network topology. The confidence score correlates with the agreement in localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a large yeast genetic interaction network. An implementation of CAPPIC is available athttp://intscore.molgen.mpg.de.
- Semantic Similarity
- Confidence Score
- Interaction Cluster
- Cluster Granularity
- False Positive Interaction
Accurate interaction networks (interactomes) are fundamental to answering questions about how the biochemical machinery of cells organizes matter, processes information, and carries out transformations to perform specific functions leading to various phenotypes. Toward this goal, a number of experimental and computational[2–4] techniques have been devised and applied to map the interactions of human proteins[5–8] and those of model organisms such as yeast[9–12]. Despite their incompleteness, current interactome maps already serve as a basis for numerous methods aiming to elucidate biological processes in health and disease[14, 15]. Current interactome maps are contaminated with false positive interactions that can make up a considerable portion of the data[13, 16–20]. These false positive interactions dim the explanatory light of interaction networks and also decrease the predictive value of methods using such data. It is thus of primary importance to derive confidence values for individual interactions, which can serve to refine current interactome maps or can be used as interaction weights. For example, it has been shown recently that the performance of complex detection approaches is better in confidence-weighted protein-protein interaction networks than in non-weighted networks[19, 21].
Several approaches have been proposed for interaction confidence assessment, many of which are reviewed in[19, 22, 23]. Most of these methods integrate additional data like interaction homology, co-expression of genes encoding interacting proteins[17, 24, 25], or a combination of these and other evidence features[26, 27]. The outcome from such methods depends on the additional data sets. Others combine multiple topological features with additional knowledge to achieve better predictions[20, 28]. Methods which are able to use network topology alone to predict interaction veracity[29–32] are the tools of choice for interaction confidence assessment if other types of data are limited or biased.
At various levels (globally as well as locally), the topology of interaction networks encodes biological properties which are largely independent of the biochemical function of the individual members of the network[33, 34]. This has been demonstrated through analysis of global properties exploiting topological features such as node degree or distance[36, 37]. The biological importance of network topology may be even more clear for local structures, as in the case of specific wiring patterns of interaction partners. Likewise, modularity of interaction networks is currently the most successful concept for addressing the dynamics of cellular processes[8, 38, 39].
Goldberg and Roth proposed a connectivity based approach for interaction confidence assessment where the number of common neighbors of a pair of predicted interaction partners counts in support of the interaction. They defined interaction confidence as the level of enrichment of common network neighbors of interacting proteins. It is quantified by the hypergeometric distribution P-value given the number of common neighbors and total network neighbors of both interacting proteins. The underlying principle of the approach has been established in seminal studies demonstrating that biological networks are marked with short interaction paths separating random pairs of proteins in the network (small-world property), and densely connected local neighborhoods (neighborhood cohesiveness property). Real protein-protein interactions are expected to meet the network cohesiveness property more frequently than false positives. More recently, Kuchaiev and co-authors proposed another method that embeds interaction networks into a low-dimensional Euclidean space based on network metrics (shortest path length) and then calculates confidence of interactions depending on the Euclidean distance between proteins within that space. The basis of the approach is the geometric graph model that was proposed to better reflect biological networks than e.g. the small-world model. Although the biological basis of the geometric graph model remains elusive, the authors show that it measures network distance more reliably. Both of these topology based methods assign confidence as numerical values to protein-protein interactions in a network and are additionally able to predict new interaction candidates by assigning confidence scores to non-interactions. However, both methods have certain shortcomings. The method by Goldberg and Roth is able to assess the confidence of those interactions whose participants have common neighbors only. Often, however, interacting proteins do not share neighbors. The method of Kuchaiev et al. appears limited in that it requires fixing six free parameters. These include algorithm-specific parameters as well as the prior probability for interactions which depends on knowledge about the interactome size.
Here, we propose CAPPIC (cluster-based assessment of protein-protein interaction confidence) – a novel approach that exploits the inherent modular structure of interactomes for confidence assessment of protein-protein interactions. Our method combines the basic principles of the topology based methods described above: high neighborhood interconnectedness of a couple of proteins and short distance between them (the features exploited by Goldberg and Roth and Kuchaiev et al., respectively) are indicators that both proteins participate in the same module. We apply Markov clustering to the line graph of an interaction network to dissect it into modules of interactions. As demonstrated in, this strategy can generate interaction clusters that significantly overlap with known biological pathways. Notably, the interaction clusters overlap in their protein constitution. This is biologically more meaningful than clustering the proteins into disjoint modules because pathways and protein machineries are known to overlap[10, 21]. The rationale behind our approach is that proteins that are specific to certain modules are expected to have more interactions with proteins that are specific to the same modules than with other proteins. Intuitively, we assign low confidence to interactions that disagree with the modular structure of biological networks and high confidence to those that comply with it. This rationale has also been used as a basis of approaches for the detection of binary interactions or protein complexes from complex purification data or to reveal dynamic interaction patterns during the human spliceosome cycle. While the aim of CAPPIC is to detect false positive interactions, a different approach, which is however also based on the principle of high link density within network modules, has been proposed for identifying false negatives.
Yeast interactome maps used in this study for method evaluation
links with ≥ 3
Assessing protein interaction confidence by random walk interaction clustering
The value of the fidelity Fp,clies between 0 and 1, with values near or equal to 1 if a protein p is specific to cluster c, i.e. if it has relatively many links in that cluster. For a fixed Lp,c it holds that the smaller the cluster (smaller L·,c), the greater the fidelity value. Finally, if all the links of two proteins lie within a cluster, the fidelity is greater for the protein with the higher degree.
Interactions get high confidence values if both proteins are specific to the cluster containing the interaction, and low confidence values when one or both of the proteins are not specific to the cluster.
Optimal clustering granularity is reliably determined through partial network rewiring
The interaction confidence scores calculated by CAPPIC are dependent on the granularity of the interaction clustering. It has been previously shown that modules in many complex networks, including protein interaction maps, are organized in a hierarchical manner. Accordingly, interaction clustering can yield protein complexes, cellular machineries, pathways, or higher-order biological processes depending on the clustering granularity. To estimate the clustering granularity for a network that will result in the best discrimination between true and false interactions, we first randomly rewire a small part of the links in that network to generate a false interaction set. In the rewiring procedure, pairs of interactions are selected at random and two of the proteins are swapped so that no real interaction is reconstituted and the network stays connected. This way, two false interactions are generated for two real ones while the degree of each protein is preserved. Then, we calculate interaction confidence values of the resulting partially rewired network as described above using different inflation values. The inflation parameter of the Markov clustering algorithm essentially controls clustering granularity. For every inflation value, we quantify the significance of the difference between confidence score distributions of the rewired and the remaining non-rewired links. This is done with the Wilcoxon rank-sum test under the alternative hypothesis that the confidence scores of the non-rewired links are greater than the confidence scores of the rewired links. The inflation value minimizing the Wilcoxon test P-value is considered optimal.
Experiments have shown that randomly rewiring 3% of the links in the granularity estimation procedure described above is a good choice because this yields a false interaction set of reasonable size while keeping most of the network intact. If the set of false interactions obtained through random rewiring is too small, the granularity estimation will lack statistical power, while if too many interactions are rewired, the network’s original modular structure will be altered which will affect the granularity estimate. For all networks CAPPIC was applied on, random rewiring of 1%, 3%, 5%, or 10% of the interactions yielded very similar optimal granularity estimates.
Our granularity estimation strategy builds upon the assumption that the optimal granularity value inferred from a partially rewired network instance (where both false positive and false negative rates are increased compared to the real network) is transferable to the real network. We aimed to scrutinize this reasoning and verified for all reference networks that 1) the estimated optimal granularity was rather independent of the random choice of links for rewiring; and 2) that interaction clusters were similar for the intact and the partially rewired networks clustered with the same inflation value (see Additional file3: Supplementary Text).
True positive interactions are assigned higher confidence than false positives
In the case of well-studied organisms such as yeast, data on protein complexes can be used to define the positive interaction sets alternatively to literature evidence as used above. We used two complex-based positive sets from yeast complexes obtained from CYC2008 and from ref.. The performance of CAPPIC (and often of the reference methods) was better with the complex-based compared to the literature-based positive set for almost all networks (Additional file4: Figure S1). For example, the AUC for CAPPIC increased from 82% to 87-89% for CPDB-yeast and from 66% to 70-72% for Costanzo when the literature-based positive reference set was replaced by a complex-based one; improvements by 1-2% AUC were also observed for the Tarassov-all, Tarassov-hq and Collins networks (Additional file4: Figure S1 versus Figure2 in the Main text). However, despite the better performance with complex-based positive reference sets, such sets are not well-suited for measuring the performance on networks obtained by techniques such as yeast-two-hybrid. This could be the reason for the slight decrease in performance (by 1-2% AUC) on the Yu-Ito-Uetz yeast-two-hybrid network compared to a literature-based positive set (Additional file4: Figure S1 versus Figure2 in the Main text). Moreover, the complex-based performance estimate may be positively biased since protein complexes in the reference data may have been defined at least partially on the basis of the analysed interaction networks.
Cluster based confidence scores corroborate experimental interaction evidence
High-confidence interactions are more consistent in biological process and cellular compartment annotation
Furthermore, if low-confidence interactions are removed from interaction clusters, the latter become more consistent regarding the pathway annotations of the contained proteins (see Additional file3: Supplementary Text). Our approach can thus be used to obtain more refined functional modules in interaction data sets.
The performance of CAPPIC is consistent between yeast and human networks
Network topology-based approaches are motivated by the fact that the structure of interaction networks is not random but reflects biological functionality[33, 34]. Modularity is a topological property that is inherent to protein-protein interaction networks[10, 39, 56]. We propose a novel method (CAPPIC) to assess the confidence of individual protein interactions in an interaction network. Our method exploits network modularity alone for estimating the confidence of interactions and does not require any additional knowledge about the interacting proteins or the techniques used to generate the data. We demonstrate the power of CAPPIC in discriminating between true and false interactions on the basis of five physical protein interaction networks and one genetic interaction map from yeast, as well as two distinct interaction data sets from human.
CAPPIC compares well to previous topology-based approaches by Goldberg and Roth and Kuchaiev et al. in assigning continuous confidence scores to all interactions in a given physical interaction network. The method of Goldberg and Roth is dependent on shared network neighbors of interacting proteins; however, many interacting proteins do not share neighbors. As a result, many interactions are scored with a confidence value of zero. However, integrative approaches operating on networks usually take probabilistic rather than binary data as input. Thus, the goal of confidence assessment is often to assign a continuous score to all interactions rather than to filter for a small subset. In particular, all proteins with a single interaction partner are disregarded by Goldberg and Roth’s method, albeit these single protein associations could give important clues about the function of these proteins. Both methods, Kuchaiev et al. and CAPPIC, are able to assign continuous scores also to such interactions. In contrast to the method of Kuchaiev et al., CAPPIC does not require any parameter input. The only parameter that influences the resulting confidence scores – clustering granularity – is optimized internally for each individual input network. Our results have shown that the number of clusters obtained at the optimal granularity tends to be small for all reference networks, ranging from 10 to 50 clusters (see Additional file5: Figure S2 and Figure ST1 in Additional file3: Supplementary Text). This alleviated our initial concerns that interactions executing essential crosstalks between related pathways could be assigned low confidence. Because the optimal granularity tends to be very coarse, closely related pathways will probably not be separated but clustered together.
CAPPIC should be applicable for weighting any binary network with an inherent modular structure (for examples, see). Notably, it does not consider the technique used to generate the network (unlike other approaches that integrate a fixed, subjective judgment on the reliability of different techniques, e.g. ref.). CAPPIC fails to generate reliable confidence scores in cases where modularity is not pronounced, i.e. if many of the real links within biological modules (complexes, pathways, etc.) are missing. This is probably the case with the Yu-Ito-Uetz and Y2H-human reference networks: here, the topological signal that our method exploits seems to be weaker and it achieves only 60-62% AUC. Absence of modularity in this example is evidenced by the relatively low clustering coefficient of 0.08 which is nine times lower than that of the Collins network where CAPPIC achieves 94% AUC and six times lower than that of the Mazloom network (90% AUC). Moreover, the Yu-Ito-Uetz data set is the sparsest of all yeast reference networks (Table1). To conclude, results on all example networks suggest that CAPPIC is well suited to score datasets with moderate to high interaction density.
Unlike the reference methods, CAPPIC is able to accommodate experimental evidence weights of interactions. Interaction detection techniques often associate such weights with predicted interactions, reflecting for example the number of times an interaction is observed in repetitions of a yeast-two-hybrid experiment[7, 9, 13] or the reporter intensity value in the case of a protein-fragment complementation assay. If available, such weights can be exploited by our method in its random walk based interaction clustering step. This can improve the interaction clustering result and consequently increase the performance of confidence assessment. However, since we set out to estimate the performance of CAPPIC in comparison to other methods that cannot accommodate interaction weights, we did not make use of this advantage in this work and considered all interactions equal. Moreover, the ability to incorporate experimental interaction weights helps to avoid interaction data pre-filtering, commonly executed to derive binary interaction networks (where pairs of proteins either interact or not). Such filtering of probabilistic interaction data is inherently associated with data loss. Similarly, it is a common practice to remove interaction hubs in a dataset to improve its quality (e.g., ref.). As exemplified in Additional file6: Figure S3 for the yeast hubs PHO85 (a Cyclin-dependent kinase; 467 interactions) and UBC7 (an E2 ubiquitin ligase; 622 interactions) in the CPDB-yeast network, CAPPIC assigns on average lower scores to interactions of hubs. However, a considerable fraction of their interactions scores highly: 29% of the interactions of PHO85 and 25% of the interactions of UBC7 are assigned higher CAPPIC scores than the median score of the complete network. This suggests that a complete removal of hubs from the network could unnecessarily remove high-quality protein-protein interactions and emphasizes the utility of confidence scoring.
Our approach can be combined with other lines of interaction evidence like other topological features, protein co-expression, or interaction homology to achieve even better scoring performance. While the aggregation of different features holds the promise of even more reliable interaction confidence assessment, it depends on reference interaction sets. At present, even for yeast the construction of an appropriate reference set is still a daunting task.
Since biological interaction networks contain false positives, assessing the confidence of individual interactions in order to weight or filter interaction data is a crucial step that should precede network-based inferences. Here we propose a network topology based method called CAPPIC that estimates interaction confidence by exploiting the network’s inherent modularity. CAPPIC requires no reference interaction sets or parameter settings. Based on five large-scale physical interaction networks from yeast, we show that our method compares well to other topology-based approaches. Confidence scores calculated with CAPPIC also correlate well with the Gene Ontology co-annotation of interacting proteins, and corroborate experimental evidence of physical interactions. CAPPIC is limited neither to physical interactome maps nor to yeast networks as it also performs well on a large yeast genetic interaction network and on two human protein-protein interaction data sets.
Application of Markov clustering algorithm
To cluster a network of interactions, we use the original implementation of the Markov clustering algorithm (version 10-201 downloaded fromhttp://www.micans.org/mcl/sec_software.html). The inflation scan which aims to optimize clustering granularity is carried out in two steps: a coarse scan with step size of 0.1 within a fixed range I∈[1.1,2.0] (where I is the Markov clustering inflation value) is followed by a fine scan with step size of 0.025 around the optimal inflation value resulting from the coarse scan ± 0.1. In general, the inflation parameter takes values from the interval I∈(1.0,30.0] with higher values resulting in finer granularity. In all our experiments, the optimal inflation estimate was far below 2.0 (see Figure ST1 in Additional file3: Supplementary Text), motivating the choice of this value as an upper boundary of the inflation scan.
Receiver operating characteristic analysis
To conduct ROC analysis, we constructed true and false interaction sets. The positive set comprised interactions published in at least three papers in total. An exception was made for the Costanzo network because of the scarcity of genetic interaction data: the positive set in this case consisted of interactions that are also reported in. Literature evidences were retrieved with the interaction evidence mining ConsensusPathDB plugin. The negative interaction set was constructed by randomly rewiring 3% of the interactions in the respective network. For each partially rewired network, we ranked interactions according to confidence as calculated with CAPPIC and reference methods and created receiver operating characteristic (ROC) curves. The performance of a given confidence assessment method in ranking positive interactions higher than negative ones was quantified with the area under the ROC curve (AUC). The AUC is around 50% if a method does not perform better than random interaction ranking, and is closer to 100% the better it ranks positive interactions higher than negative ones. Since the constitution of the negative and positive sets involves a random process (that is, the random selection of interactions for rewiring), we repeated the procedure 100 times and averaged ROC results.
Application of reference methods
We set the number of yeast genes to 6,000 in the method by Goldberg and Roth. The parameters of the method by Kuchaiev et al. (implemented as Matlab scripts downloaded fromhttp://www.kuchaev.com/Denoising) were set as follows: priorEdge=0.002945 (which results when the estimated yeast interactome size of 53,000 interactions is divided by the number of all possible protein pairs, 6,000 choose 2); priorNonEdge=1-priorEdge; dim=5 (default); d=3 (default); learnSetSize=min(5,000 or half the number of interactions); delta=1.0; and stopEps=0.01 (default). In the case of Costanzo, dim=3 because the program (run on a standard AMD X2 5600+ machine with 8GB of RAM running Matlab version 18.104.22.1689 under Linux) did not return results within five days for a higher number of dimensions.
Assessing semantic similarity of Gene Ontology annotations
For each network, we obtained the GO semantic similarity of biological process and cellular component annotations of interacting proteins using the method proposed by Resnik implemented in the software package GOSemSim version 1.8.0. GO annotations inferred from physical interaction (GO evidence code ‘IPI’) were excluded from the semantic similarity calculation to avoid circularity. For each network, interactions were ranked by increasing confidence score and divided into five equal sized bins. The mean semantic similarity values for interacting proteins within each bin were calculated. Additionally, the mean GO semantic similarity for random pairs of proteins from the respective network was assessed by completely rewiring the networks while preserving each protein’s degree and then calculating the mean GO semantic similarity of links in those randomized networks.
This work was funded by the European Commission under its 7FP grant diXa (grant number 283775) and the German Ministry for Education and Research under its grants MedSys - PREDICT (grant number 0315428A) and NGFNplus (NeuroNet TP3, grant number 01GS08171).
- Stelzl U, Wanker EE: The value of high quality protein-protein interaction networks for systems biology. Curr Opin Chem Biol 2006, 10(6):551–558. [. [PMID: 17055769] http://www.ncbi.nlm.nih.gov/pubmed/17055769] 10.1016/j.cbpa.2006.10.005View ArticlePubMedGoogle Scholar
- Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, Chung S, Emili A, Snyder M, Greenblatt JF, Gerstein M: A Bayesian networks approach for predicting protein-protein interactions from genomic data. Sci (New York, N.Y.) 2003, 302(5644):449–453. [. [PMID: 14564010] http://www.ncbi.nlm.nih.gov/pubmed/14564010] 10.1126/science.1087361View ArticleGoogle Scholar
- Kamburov A, Goldovsky L, Freilich S, Kapazoglou A, Kunin V, Enright AJ, Tsaftaris A, Ouzounis CA: Denoising inferred functional association networks obtained by gene fusion analysis. BMC Genomics 2007, 8: 460. [. [PMID: 18081932] http://www.ncbi.nlm.nih.gov/pubmed/18081932] 10.1186/1471-2164-8-460PubMed CentralView ArticlePubMedGoogle Scholar
- Pazos F, Juan D, Izarzugaza JMG, Leon E, Valencia A: Methods in Mol Biol (Clifton, N.J.). 2008, 484: 523–535. [. [PMID: 18592199] http://www.ncbi.nlm.nih.gov/pubmed/18592199] 10.1007/978-1-59745-398-1_31View ArticleGoogle Scholar
- Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S, Timm J, Mintzlaff S, Abraham C, Bock N, Kietzmann S, Goedde A, Toksöz E, Droege A, Krobitsch S, Korn B, Birchmeier W, Lehrach H, Wanker EE: A human protein-protein interaction network: a resource for annotating the proteome. Cell 2005, 122(6):957–968. [. [PMID: 16169070] http://www.ncbi.nlm.nih.gov/pubmed/16169070] 10.1016/j.cell.2005.08.029View ArticlePubMedGoogle Scholar
- Bandyopadhyay S, Chiang Cy, Srivastava J, Gersten M, White S, Kurschner C, Martin CH, Smoot M, Sahasrabudhe S, Barber DL, Chanda SK, Ideker T, Bell R: A human MAP kinase interactome. Nat Methods 2010, 7(10):801–805. [. [PMID: 20936779] http://www.ncbi.nlm.nih.gov/pubmed/20936779] 10.1038/nmeth.1506PubMed CentralView ArticlePubMedGoogle Scholar
- Vinayagam A, Stelzl U, Foulle R, Plassmann S, Zenkner M, Timm J, Assmus HE, Wanker EE, Andrade-Navarro MA: A directed protein interaction network for investigating intracellular signal transduction. Sci Signaling 2011, 4(189):rs8. [. [PMID:21900206] http://www.ncbi.nlm.nih.gov/pubmed/21900206] 10.1126/scisignal.2001699View ArticleGoogle Scholar
- Hegele A, Kamburov A, Grossmann A, Sourlis C, Wowro S, Weimann M, Will CL, Pena V, Lührmann R, Stelzl U: Dynamic protein-protein interaction wiring of the human spliceosome. Mol Cell 2012, 45(4):567–580. [. [PMID: 22365833] http://www.ncbi.nlm.nih.gov/pubmed/22365833] 10.1016/j.molcel.2011.12.034View ArticlePubMedGoogle Scholar
- Yu H, Braun P, Yildirim MA, Lemmens I, Venkatesan K, Sahalie J, Gebreab F, Li N, Simonis N, Hao T, Rual J, Dricot A, Vazquez A, Murray RR, Simon C, Tardivo L, Tam S, Svrzikapa N, Fan C, Smet Ad, Motyl A, Hudson ME, Park J, Xin X, Cusick ME, Moore T, Boone C, Snyder M, Roth FP, Barabasi A, Hirozane-Kishikawa T, et al.: High-quality binary protein interaction map of the yeast interactome network. Sci (New York, N.Y.) 2008, 322(5898):104–110. [. [PMID: 18719252] http://www.ncbi.nlm.nih.gov/pubmed/18719252] 10.1126/science.1158684View ArticleGoogle Scholar
- Gavin A, Aloy P, Grandi P, Krause R, Boesche M, Marzioch M, Rau C, Jensen LJ, Bastuck S, Dümpelfeld B, Edelmann A, Heurtier M, Hoffman V, Hoefert C, Klein K, Hudak M, Michon A, Schelder M, Schirle M, Remor M, Rudi T, Hooper S, Bauer A, Bouwmeester T, Casari G, Drewes G, Neubauer G, Rick JM, Kuster B, Bork P, Russell RB, Superti-Furga G: Proteome survey reveals modularity of the yeast cell machinery. Nature 2006, 440(7084):631–636. [. [PMID: 16429126] http://www.ncbi.nlm.nih.gov/pubmed/16429126] 10.1038/nature04532View ArticlePubMedGoogle Scholar
- Krogan NJ, Cagney G, Yu H, Zhong G, Guo X, Ignatchenko A, Li J, Pu S, Datta N, Tikuisis AP, Punna T, Shales M, Zhang X, Davey M, Robinson MD, Paccanaro A, Bray JE, Sheung A, Beattie B, Richards DP, Canadien V, Lalev A, Mena F, Wong P, Starostine A, Canete MM, Vlasblom J, Wu S, Orsi C, et al.: Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 2006, 440(7084):637–643. [. [PMID: 16554755] http://www.ncbi.nlm.nih.gov/pubmed/16554755] 10.1038/nature04670View ArticlePubMedGoogle Scholar
- Tarassov K, Messier V, Landry CR, Radinovic S, Molina MMS, Shames I, Malitskaya Y, Vogel J, Bussey H, Michnick SW: An in vivo map of the yeast protein interactome. Sci (New York, N.Y.) 2008, 320(5882):1465–1470. 10.1126/science.1153878View ArticleGoogle Scholar
- Venkatesan K, Rual J, Vazquez A, Stelzl U, Lemmens I, Hirozane-Kishikawa T, Hao T, Zenkner M, Xin X, Goh K, Yildirim MA, Simonis N, Heinzmann K, Gebreab F, Sahalie JM, Cevik S, Simon C, Smet Ad, Dann E, Smolyar A, Vinayagam A, Yu H, zeto D, Borick H, Dricot A, Klitgord N, Murray RR, Lin C, Lalowski M, Timm J, et al.: An empirical framework for binary interactome mapping. Nat Methods 2009, 6: 83–90. [. [PMID: 19060904] http://www.ncbi.nlm.nih.gov/pubmed/19060904] 10.1038/nmeth.1280PubMed CentralView ArticlePubMedGoogle Scholar
- Ideker T, Sharan R: Protein networks in disease. Genome Res 2008, 18(4):644–652. [. [PMID: 18381899] http://www.ncbi.nlm.nih.gov/pubmed/18381899] 10.1101/gr.071852.107PubMed CentralView ArticlePubMedGoogle Scholar
- Vidal M, Cusick ME, Barabasi A: Interactome networks and human disease. Cell 2011, 144(6):986–998. [. [PMID: 21414488] http://www.ncbi.nlm.nih.gov/pubmed/21414488] 10.1016/j.cell.2011.02.016PubMed CentralView ArticlePubMedGoogle Scholar
- Mering Cv, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P: Comparative assessment of large-scale data sets of protein-protein interactions. Nature 2002, 417(6887):399–403. [. [PMID: 12000970] http://www.ncbi.nlm.nih.gov/pubmed/12000970]View ArticleGoogle Scholar
- Deane CM, Salwinski L, Xenarios I, Eisenberg D: Protein interactions: two methods for assessment of the reliability of high throughput observations. Mol & Cell Proteomics: MCP 2002, 1(5):349–356. [. [PMID: 12118076] http://www.ncbi.nlm.nih.gov/pubmed/12118076] 10.1074/mcp.M100037-MCP200View ArticlePubMedGoogle Scholar
- Jansen R, Gerstein M: Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. Curr Opin Microbiol 2004, 7(5):535–545. [. [PMID: 15451510] http://www.ncbi.nlm.nih.gov/pubmed/15451510] 10.1016/j.mib.2004.08.012View ArticlePubMedGoogle Scholar
- Kritikos GD, Moschopoulos C, Vazirgiannis M, Kossida S: Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme. BMC Bioinf 2011, 12: 239. [. [PMID: 21679454] http://www.ncbi.nlm.nih.gov/pubmed/21679454] 10.1186/1471-2105-12-239View ArticleGoogle Scholar
- Yu J, Murali T, Finley J, Russell L: Assigning confidence scores to protein-protein interactions. Methods Mol Biol (Clifton, N.J.) 2012, 812: 161–174. [. [PMID: 22218859] http://www.ncbi.nlm.nih.gov/pubmed/22218859] 10.1007/978-1-61779-455-1_9View ArticleGoogle Scholar
- Nepusz T, Yu H, Paccanaro A: Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods 2012, 9: 471–472. [. [PMID: 22426491] http://www.ncbi.nlm.nih.gov/pubmed/22426491] 10.1038/nmeth.1938PubMed CentralView ArticlePubMedGoogle Scholar
- Suthram S, Shlomi T, Ruppin E, Sharan R, Ideker T: A direct comparison of protein interaction confidence assignment schemes. BMC Bioinf 2006, 7: 360. [. [PMID: 16872496] http://www.ncbi.nlm.nih.gov/pubmed/16872496] 10.1186/1471-2105-7-360View ArticleGoogle Scholar
- Chua HN, Wong L: Increasing the reliability of protein interactomes. Drug Discovery Today 2008, 13(15–16):652–658. [. PMID: 18595769 http://www.ncbi.nlm.nih.gov/pubmed/18595769] 10.1016/j.drudis.2008.05.004View ArticlePubMedGoogle Scholar
- Kemmeren P, Berkum NLv, Vilo J, Bijma T, Donders R, Brazma A, Holstege FCP: Protein interaction verification and functional annotation by integrated analysis of genome-scale data. Mol Cell 2002, 9(5):1133–1143. [. [PMID: 12049748] http://www.ncbi.nlm.nih.gov/pubmed/12049748] 10.1016/S1097-2765(02)00531-2View ArticlePubMedGoogle Scholar
- Deng M, Sun F, Chen T: Assessment of the reliability of protein-protein interactions and protein function prediction. Pacific Symp on Biocomputing. Pacific Symp Biocomputing 2003, 140–151. [. [PMID: 12603024] http://www.ncbi.nlm.nih.gov/pubmed/12603024]Google Scholar
- Sharan R, Suthram S, Kelley RM, Kuhn T, McCuine S, Uetz P, Sittler T, Karp RM, Ideker T: Conserved patterns of protein interaction in multiple species. Proc Nat Acad Sci USA 2005, 102(6):1974–1979. [. [PMID: 15687504] http://www.ncbi.nlm.nih.gov/pubmed/15687504] 10.1073/pnas.0409522102PubMed CentralView ArticlePubMedGoogle Scholar
- Li D, Liu W, Liu Z, Wang J, Liu Q, Zhu Y, He F: PRINCESS, a protein interaction confidence evaluation system with multiple data sources. Mol & Cell Proteomics 2008, 7(6):1043–1052. [http://www.mcponline.org/content/7/6/1043.abstract]View ArticleGoogle Scholar
- Bader JS, Chaudhuri A, Rothberg JM, Chant J: Gaining confidence in high-throughput protein interaction networks. Nat Biotechnol 2004, 22: 78–85. [. [PMID: 14704708] http://www.ncbi.nlm.nih.gov/pubmed/14704708] 10.1038/nbt924View ArticlePubMedGoogle Scholar
- Goldberg DS, Roth FP: Assessing experimentally derived interactions in a small world. Proc Nat Acad Sci USA 2003, 100(8):4372–4376. [. [PMID: 12676999] http://www.ncbi.nlm.nih.gov/pubmed/12676999] 10.1073/pnas.0735871100PubMed CentralView ArticlePubMedGoogle Scholar
- Saito R, Suzuki H, Hayashizaki Y: Construction of reliable protein-protein interaction networks with a new interaction generality measure. Bioinformatics (Oxford, England) 2003, 19(6):756–763. [. [PMID: 12691988] http://www.ncbi.nlm.nih.gov/pubmed/12691988] 10.1093/bioinformatics/btg070View ArticleGoogle Scholar
- Chen J, Hsu W, Lee ML, Ng S: Discovering reliable protein interactions from high-throughput experimental data using network topology. Artif Intelligence Med 2005, 35(1–2):37–47. [. [PMID: 16055319] http://www.ncbi.nlm.nih.gov/pubmed/16055319] 10.1016/j.artmed.2005.02.004View ArticleGoogle Scholar
- Kuchaiev O, Rasajski M, Higham DJ, Przulj N: Geometric de-noising of protein-protein interaction networks. PLoS Comput Biol 2009, 5(8):e1000454. [. [PMID: 19662157] http://www.ncbi.nlm.nih.gov/pubmed/19662157] 10.1371/journal.pcbi.1000454PubMed CentralView ArticlePubMedGoogle Scholar
- Barabasi A, Oltvai ZN: Network biology: understanding the cell’s functional organization. Nat Rev Genet 2004, 5(2):101–113. [. [PMID: 14735121] http://www.ncbi.nlm.nih.gov/pubmed/14735121] 10.1038/nrg1272View ArticlePubMedGoogle Scholar
- Alon U: Network motifs: theory and experimental approaches. Nat Rev Genet 2007, 8(6):450–461. [. [PMID: 17510665] http://www.ncbi.nlm.nih.gov/pubmed/17510665] 10.1038/nrg2102View ArticlePubMedGoogle Scholar
- Goh K, Cusick ME, Valle D, Childs B, Vidal M, Barabasi A: The human disease network. Proc Nat Acad Sci USA 2007, 104(21):8685–8690. [. [PMID: 17502601] http://www.ncbi.nlm.nih.gov/pubmed/17502601] 10.1073/pnas.0701361104PubMed CentralView ArticlePubMedGoogle Scholar
- Berger SI, Ma’ayan A, Iyengar R: Systems pharmacology of arrhythmias. Sci Signaling 2010, 3(118):ra30. [. [PMID: 20407125] http://www.ncbi.nlm.nih.gov/pubmed/20407125] 10.1126/scisignal.2000723Google Scholar
- Yosef N, Ungar L, Zalckvar E, Kimchi A, Kupiec M, Ruppin E, Sharan R: Toward accurate reconstruction of functional protein networks. Mol Syst Biol 2009, 5: 248. [. [PMID: 19293828] http://www.ncbi.nlm.nih.gov/pubmed/19293828]PubMed CentralView ArticlePubMedGoogle Scholar
- Alexander RP, Kim PM, Emonet T, Gerstein MB: Understanding modularity in molecular networks requires dynamics. Sci Signaling 2009, 2(81):pe44. [. [PMID: 19638611] http://www.ncbi.nlm.nih.gov/pubmed/19638611] 10.1126/scisignal.281pe44View ArticleGoogle Scholar
- Fortunato S: Community detection in graphs. Phys R 2010, 486(3–5):75–174. [http://www.sciencedirect.com/science/article/B6TVP-4XPYXF1–1/2/99061fac6435db4343b2374d26e64ac1] 10.1016/j.physrep.2009.11.002View ArticleGoogle Scholar
- Watts DJ, Strogatz SH: Collective dynamics of ’small-world’ networks. Nature 1998, 393(6684):440–442. [. [PMID: 9623998] http://www.ncbi.nlm.nih.gov/pubmed/9623998] 10.1038/30918View ArticlePubMedGoogle Scholar
- Higham DJ, Rasajski M, Przulj N: Fitting a geometric graph to a protein-protein interaction network. Bioinformatics (Oxford, England) 2008, 24(8):1093–1099. [. [PMID: 18344248] http://www.ncbi.nlm.nih.gov/pubmed/18344248] 10.1093/bioinformatics/btn079View ArticleGoogle Scholar
- Dongen Sv: A Cluster algorithm for graphs. PhD thesis, Centrum voor Wiskunde en Informatica. Amsterdam, Netherlands; 2000.Google Scholar
- Whitney H: Congruent graphs and the connectivity of graphs. Am J Mathematics 1932, 54: 150–168. [. [ArticleType: research-article /Full publication date: Jan. 1932 /Copyright 1932 The Johns Hopkins University Press] http://www.jstor.org/stable/2371086] 10.2307/2371086View ArticleGoogle Scholar
- Pereira-Leal JB, Enright AJ, Ouzounis CA: Detection of functional modules from protein interaction networks. Proteins 2004, 54: 49–57. [. [PMID: 14705023] http://www.ncbi.nlm.nih.gov/pubmed/14705023]View ArticlePubMedGoogle Scholar
- Friedel CC, Krumsiek J, Zimmer R: Bootstrapping the interactome: unsupervised identification of protein complexes in yeast. J of Comput Biol: A J of Comput Mol Cell Biol 2009, 16(8):971–987. [. [PMID: 19630542] http://www.ncbi.nlm.nih.gov/pubmed/19630542]View ArticleGoogle Scholar
- Yu H, Paccanaro A, Trifonov V, Gerstein M: Predicting interactions in protein networks by completing defective cliques. Bioinf (Oxford, England) 2006, 22(7):823–829. [. [PMID: 16455753] http://www.ncbi.nlm.nih.gov/pubmed/16455753] 10.1093/bioinformatics/btl014View ArticleGoogle Scholar
- Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Nat Acad Sci USA 2001, 98(8):4569–4574. [. [PMID: 11283351] http://www.ncbi.nlm.nih.gov/pubmed/11283351] 10.1073/pnas.061034498PubMed CentralView ArticlePubMedGoogle Scholar
- Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, Qureshi-Emili A, Li Y, Godwin B, Conover D, Kalbfleisch T, Vijayadamodar G, Yang M, Johnston M, Fields S, Rothberg JM: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000, 403(6770):623–627. [. [PMID: 10688190] http://www.ncbi.nlm.nih.gov/pubmed/10688190] 10.1038/35001009View ArticlePubMedGoogle Scholar
- Collins SR, Kemmeren P, Zhao X, Greenblatt JF, Spencer F, Holstege FCP, Weissman JS, Krogan NJ: Mol & Cell Proteomics: MCP. 2007, 6(3):439–450. [. [PMID: 17200106] http://www.ncbi.nlm.nih.gov/pubmed/17200106] View ArticlePubMedGoogle Scholar
- Stark C, Breitkreutz B, Chatr-Aryamontri A, Boucher L, Oughtred R, Livstone MS, Nixon J, Auken KV, Wang X, Shi X, Reguly T, Rust JM, Winter A, Dolinski K, Tyers M: The BioGRID interaction database: 2011 update. Nucleic Acids Res 2011, 39(Database issue):D698—704. [. [PMID: 21071413] http://www.ncbi.nlm.nih.gov/pubmed/21071413]]PubMedGoogle Scholar
- Kamburov A, Pentchev K, Galicka H, Wierling C, Lehrach H, Herwig R: ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res 2011, 39(Database issue):D712–717. [. [PMID: 21071422] http://www.ncbi.nlm.nih.gov/pubmed/21071422]PubMed CentralView ArticlePubMedGoogle Scholar
- Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED, Sevier CS, Ding H, Koh JLY, Toufighi K, Mostafavi S, Prinz J, Onge RPS, VanderSluis B, Makhnevych T, Vizeacoumar FJ, Alizadeh S, Bahr S, Brost RL, Chen Y, Cokol M, Deshpande R, Li Z, Lin Z, Liang W, Marback M, Paw J, Luis BS, Shuteriqi E, Dyk Nv, et al.: The genetic landscape of a cell. Sci (New York, N.Y.) 2010, 327(5964):425–431. [. [PMID: 20093466] http://www.ncbi.nlm.nih.gov/pubmed/20093466] 10.1126/science.1180823View ArticleGoogle Scholar
- Mazloom AR, Dannenfelser R, Clark NR, Grigoryan AV, Linder KM, Cardozo TJ, Bond JC, Boran ADW, Iyengar R, Malovannaya A, Lanz RB, Ma’ayan A: Recovering protein-protein and domain-domain interactions from aggregation of IP-MS proteomics of coregulator complexes. PLoS Comput Biol 2011, 7(12):e1002319. [. [PMID: 22219718] http://www.ncbi.nlm.nih.gov/pubmed/22219718] 10.1371/journal.pcbi.1002319PubMed CentralView ArticlePubMedGoogle Scholar
- Malovannaya A, Lanz RB, Jung SY, Bulynko Y, Le NT, Chan DW, Ding C, Shi Y, Yucer N, Krenciute G, Kim B, Li C, Chen R, Li W, Wang Y, O’Malley BW, Qin J: Analysis of the human endogenous coregulator complexome. Cell 2011, 145(5):787–799. [. [PMID: 21620140] http://www.ncbi.nlm.nih.gov/pubmed/21620140] 10.1016/j.cell.2011.05.006PubMed CentralView ArticlePubMedGoogle Scholar
- Kamburov A, Herwig R, Stelzl U: IntScore: a web tool for confidence scoring of biological interactions. Nucleic Acids Res 2012, 40(Web Server issue):W140–146. [. [PMID: 22649056] http://www.ncbi.nlm.nih.gov/pubmed/22649056]PubMed CentralView ArticlePubMedGoogle Scholar
- Ravasz E: Methods Mol Biol (Clifton, N.J.). 2009, 541: 145–160. [. [PMID: 19381526] http://www.ncbi.nlm.nih.gov/pubmed/19381526] 10.1007/978-1-59745-243-4_7View ArticleGoogle Scholar
- Pu S, Wong J, Turner B, Cho E, Wodak SJ: Up-to-date catalogues of yeast protein complexes. Nucleic Acids Res 2009, 37(3):825–831. [. [PMID: 19095691] http://www.ncbi.nlm.nih.gov/pubmed/19095691] 10.1093/nar/gkn1005PubMed CentralView ArticlePubMedGoogle Scholar
- Benschop JJ, Brabers N, van Leenen D, Bakker LV, van Deutekom HWM, van Berkum NL, Apweiler E, Lijnzaad P, Holstege FCP, Kemmeren P: A consensus of core protein complex compositions for Saccharomyces cerevisiae. Mol Cell 2010, 38(6):916–928. [. [PMID: 20620961] http://www.ncbi.nlm.nih.gov/pubmed/20620961] 10.1016/j.molcel.2010.06.002View ArticlePubMedGoogle Scholar
- Oliver S: Guilt-by-association goes global. Nature 2000, 403(6770):601–603. [. [PMID: 10688178] http://www.ncbi.nlm.nih.gov/pubmed/10688178] 10.1038/35001165View ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25: 25–29. [. [PMID: 10802651] http://www.ncbi.nlm.nih.gov/pubmed/10802651] 10.1038/75556PubMed CentralView ArticlePubMedGoogle Scholar
- Ahn Y, Bagrow JP, Lehmann S: Link communities reveal multiscale complexity in networks. Nature 2010, 466(7307):761–764. [. [PMID: 20562860] http://www.ncbi.nlm.nih.gov/pubmed/20562860] 10.1038/nature09182View ArticlePubMedGoogle Scholar
- Schaefer MH, Fontaine J, Vinayagam A, Porras P, Wanker EE, Andrade-Navarro MA: HIPPIE: Integrating protein interaction networks with experiment based quality scores. PloS One 2012, 7(2):e31826. [. [PMID: 22348130] http://www.ncbi.nlm.nih.gov/pubmed/22348130] 10.1371/journal.pone.0031826PubMed CentralView ArticlePubMedGoogle Scholar
- Collins SR, Miller KM, Maas NL, Roguev A, Fillingham J, Chu CS, Schuldiner M, Gebbia M, Recht J, Shales M, Ding H, Xu H, Han J, Ingvarsdottir K, Cheng B, Andrews B, Boone C, Berger SL, Hieter P, Zhang Z, Brown GW, Ingles CJ, Emili A, Allis CD, Toczyski DP, Weissman JS, Greenblatt JF, Krogan NJ: Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 2007, 446(7137):806–810. [. [PMID: 17314980] http://www.ncbi.nlm.nih.gov/pubmed/17314980] 10.1038/nature05649View ArticlePubMedGoogle Scholar
- Pentchev K, Ono K, Herwig R, Ideker T, Kamburov A: Evidence mining and novelty assessment of protein-protein interactions with the ConsensusPathDB plugin for Cytoscape. Bioinformatics (Oxford, England) 2010, 26(21):2796–2797. [. [PMID: 20847220] http://www.ncbi.nlm.nih.gov/pubmed/20847220] 10.1093/bioinformatics/btq522View ArticleGoogle Scholar
- Hart GT, Ramani AK, Marcotte EM: How complete are current yeast and human protein-interaction networks? Genome Biol 2006, 7(11):120. [. [PMID: 17147767] http://www.ncbi.nlm.nih.gov/pubmed/17147767] 10.1186/gb-2006-7-11-120PubMed CentralView ArticlePubMedGoogle Scholar
- Resnik P: Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J of Artif Intelligence Res 1999, 11: 95–130. [http://citeseerx.ist.psu.edu/viewdoc/summary?doi:10.1.1.50.3785]Google Scholar
- Yu G, Li F, Qin Y, Bo X, Wu Y, Wang S: GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinf (Oxford, England) 2010, 26(7):976–978. [. [PMID: 20179076] http://www.ncbi.nlm.nih.gov/pubmed/20179076] 10.1093/bioinformatics/btq064View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.