Comparative assessment of large-scale data sets of protein–protein interactions

Mering, Christian von; Krause, Roland; Snel, Berend; Cornell, Michael; Oliver, Stephen G.; Fields, Stanley; Bork, Peer

doi:10.1038/nature750

Cited by 2,147 publications

(1,775 citation statements)

References 44 publications

Supporting

Mentioning

1,725

Contrasting

Unclassified

Order By: Relevance

“…To this end, we consider a real data set of protein-protein interactions (Von Mering et al, 2002) in which a labeling x ∈ {1, −1} |V | of V is known. By applying COSNet we approximate the optimal parameters (α,γ).…”

Section: Validation Of Cosnet Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

A neural network algorithm for semi-supervised node label learning from unbalanced data

et al. 2013

View full text Add to dashboard Cite

Given a weighted graph and a partial node labeling, the graph classification problem consists in predicting the labels of all the nodes. In several application domains, from gene to social network analysis, the labeling is unbalanced: for instance positive labels may be much less than negatives. In this paper we present COSNet (COst Sensitive neural Network), a neural algorithm for predicting node labels in graphs with unbalanced labels. COSNet is based on a 2-parameters family of Hopfield networks, and consists of two main steps: 1) the network parameters are learned through a costsensitive optimization procedure; 2) a suitable Hopfield network restricted to the unlabeled nodes is considered and simulated. The reached equilibrium point induces the classification of the unlabeled nodes. The restriction of the dynamics leads to a significant reduction in time complexity and allows the algorithm to nicely scale with large networks. An experimental analysis on real-world unbalanced data, in the context of the genome-wide prediction of gene functions, shows the effectiveness of the proposed approach.

show abstract

Section: Validation Of Cosnet Optimizationmentioning

confidence: 99%

“…-PPI-VM is another data set of protein-protein interactions that collects binary protein-protein interaction data for 2338 proteins from yeast two-hybrid assay, mass-spectrometry of purified complexes, correlated mRNA expression and genetic interactions (Von Mering et al, 2002). …”

Section: Data Setsmentioning

confidence: 99%

A neural network algorithm for semi-supervised node label learning from unbalanced data

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Helicobacter pylori (Rain et al, 2001), Sacchoromyces cereviciae (von Mering et al, 2002;Bu et al, 2003), and Homo sapiens (Rual et al, 2005). Here we consider the main connected component of these networks in which interactions between proteins are taken to be undirected and no selfinteractions are considered.…”

Section: Study Of Protein-protein Interaction Networkmentioning

confidence: 99%

Generalized walks-based centrality measures for complex biological networks

Estrada

2010

Journal of Theoretical Biology

View full text Add to dashboard Cite

A strategy for zooming in and out the topological environment of a node in a complex network is developed. This approach is applied here to generalize the subgraph centrality of nodes in complex networks. In this case the zooming in strategy is based on the use of some known matrix functions which allow focusing locally on the environment of a node. When a zooming out strategy is applied new matrix functions are introduced, which give a more global picture of the topological surrounds of a node. These indices permit a modulation of the scales at which the environment of a node influences its centrality. We apply them to the study of 10 protein-protein interaction (PPI) networks. We illustrate the similarities and differences between the generalized subgraph centrality indices as well as among them and some classical centrality measures. We show here that the use of centrality indices based on the zooming in strategy identifies a larger number of essential proteins in the yeast PPI network than any of the other centrality measures studied.

show abstract

“…Two distinct examples include the correlation of mRNA expression profiles to function, as in the characterization of pharmacological perturbations by identifying a novel target of dyclonine, 78 and through in silico predictions of structural or pathway interactions from whole genome comparative analyses. 79 von Mering et al 80 evaluated the roughly 80 000 interactions among yeast proteins currently available in various databases by comparing data from yeast two-hybrid systems, mass spectrometric protein complex purification techniques, mRNA expression profiles, genetic interaction data, as well as in silico interaction predictions from gene fusion, neighborhood, and co-occurrence analysis. Surprisingly, only B2400 of these interactions are supported by more than one method.…”

Section: Protein-protein Interactions: Isolation Of Protein Complexesmentioning

confidence: 99%

Pharmacoproteomics in drug development

Witzmann

Grant

2003

Pharmacogenomics J

View full text Add to dashboard Cite

The field of proteomics is taking on increased significance as the relevance of investigating and understanding protein expression in disease and drug development is appreciated. Recent advances in proteomics have been driven by the availability of numerous annotated whole-genome sequences and a broad range of technological and bioinformatic developments that underscore the complexity of the proteome. This review briefly addresses some of the various technologies that comprise Expression Proteomics and Functional Proteomics, citing examples where these emerging approaches have been applied to pharmacology, toxicology, and the development of drugs.

show abstract

Comparative assessment of large-scale data sets of protein–protein interactions

Cited by 2,147 publications

References 44 publications

A neural network algorithm for semi-supervised node label learning from unbalanced data

A neural network algorithm for semi-supervised node label learning from unbalanced data

Generalized walks-based centrality measures for complex biological networks

Pharmacoproteomics in drug development

Contact Info

Product

Resources

About