Effect of sampling on topology predictions of protein-protein interaction networks

Han, Jing‐Dong J.; Dupuy, Denis; Bertin, Nicolas; Cusick, Michael E.; Vidal, Marc

doi:10.1038/nbt1116

Cited by 311 publications

(292 citation statements)

References 50 publications

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“…However, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks (19). Therefore, computational methods for the prediction of PPIs have an important role (20).…”

mentioning

confidence: 99%

Predicting protein–protein interactions based only on sequences information

Shen

Zhang

Luo

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

882

811

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Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method.conjoint triad ͉ support vector machine T he molecular bases of cellular operations are sustained largely by different types of interactions among proteins. Thus, a major goal of functional genomics is to determine protein interaction networks for whole organisms (1). However, only recently has it become possible to combine the traditional study of proteins as isolated entities with the analysis of large protein interaction networks by using microarray and proteomic approaches (2, 3). Such kinds of studies are significantly important because many of the functions of complex systems seem to be more closely determined by their interactions rather than by the characteristics of their individual components (4). For example, metabolic pathways, signaling cascades, and transcription control processes involve complicated interaction networks (5). Recently, interaction networks have begun to be appreciated because it is necessary to address the general principles of biological systems by means of systems biology (6). Moreover, the study of protein interaction networks has been driven by potentially practical applications in drug discovery, because it might provide great insights into mechanisms of human diseases. This study may revolutionize the pipeline of drug discovery, because drugs discovered based on the protein interaction network may specifically modulate the disease-related pathway rather than simply inhibit or activate the functions of an individual target protein (7,8). Determining accurate cellular protein interaction networks with experimental methods in combination with computational approaches therefore has become a major theme of functional genomics and proteomics efforts (9).An impressive set of experimental techniques has been developed for the systematic analysis of protein-protein interactions (...

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mentioning

confidence: 99%

Predicting protein–protein interactions based only on sequences information

Shen

Zhang

Luo

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

882

811

View full text Add to dashboard Cite

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“…In scale-free networks the degree distribution (number of links per node) follows a power-law [43]. Even though the scale-free topology of the marginally sampled networks may not represent the true topology of the complete networks [30], that hubs exist in these networks is unlikely to be derived randomly [30]. "Degree centrality" has been linked to essentiality and conservation of genes [44,45].…”

Section: Uncovering Network Properties By Statistical Analysesmentioning

confidence: 99%

“…With each step, data are churned or sublimed into information with a reduction in the amount of bits but an increase in accuracy, quality and usefulness. [30]. Therefore, data integration is needed to obtain a more comprehensive, less technically biased and more accurate view of the true network.…”

Section: Inferring Biological Information By Integration Of Raw Datamentioning

confidence: 99%

Understanding biological functions through molecular networks

Han

2008

Cell Res

146

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“…Among the protein interactions produced by high-throughput methods such as the yeast 2-hybrid experiment or tagged affinity purification (TAP) [1,15,16], there are many false positives due to experimental limitations as well as biological factors (proteins that are not expressed at the same time or in the same cellular locale) [13]. In order to reduce the interference by false positives, we focused on the protein interaction network from the Database of Interacting Proteins (DIP), circa.…”

Section: Data Source and Analysismentioning

confidence: 99%

“…The latter make it harder to discover clusters in the data, while the former increase the computational requirements. Cluster validation is hampered by the fact that there is often little overlap between different experimental studies due to the limited coverage of the interactome [13]. Finally, the predicted clusters must be biologically significant: e.g., functionally homogeneous.…”

Section: Introductionmentioning

confidence: 99%

The Architecture of a Proteomic Network in the Yeast

Ramadan

Osgood

Pothen

2005

Lecture Notes in Computer Science

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Abstract.We describe an approach to clustering the yeast protein-protein interaction network in order to identify functional modules, groups of proteins forming multi-protein complexes accomplishing various functions in the cell. We have developed a clustering method that accounts for the small-world nature of the network. The algorithm makes use of the concept of k-cores in a graph, and employs recursive spectral clustering to compute the functional modules. The computed clusters are annotated using their protein memberships into known multi-protein complexes in the yeast. We also dissect the protein interaction network into a global subnetwork of hub proteins (connected to several clusters), and a local network consisting of cluster proteins.

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Effect of sampling on topology predictions of protein-protein interaction networks

Cited by 311 publications

References 50 publications

Predicting protein–protein interactions based only on sequences information

Predicting protein–protein interactions based only on sequences information

Understanding biological functions through molecular networks

The Architecture of a Proteomic Network in the Yeast

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