Prediction of Protein‐Protein Interactions

Kotlyar, Max; Rossos, Andrea E. M.; Jurišica, Igor

doi:10.1002/cpbi.38

Cited by 21 publications

(15 citation statements)

References 150 publications

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“…Databases provide disease-specific as well as general PPI information [27][28][29][30][31]. PPI data can be based on wet experimentation as well as prediction [32,33]. Integrative tools for visualization of PPI networks thereby facilitate exploration and analysis tasks.…”

Section: Ppi Network and Graph Analysismentioning

confidence: 99%

Open Data for Differential Network Analysis in Glioma

Jean-Quartier¹,

Jeanquartier²,

Holzinger³

2020

IJMS

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The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. By using selected exemplary tools and open-access resources for cancer research and differential network analysis, we highlight disturbed signaling components in brain cancer subtypes of glioma.

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Section: Ppi Network and Graph Analysismentioning

confidence: 99%

Open Data for Differential Network Analysis in Glioma

Jean-Quartier¹,

Jeanquartier²,

Holzinger³

2020

IJMS

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“…Overall, the field of PPI prediction has been highly active in the last decade, with new methods proposed each year as recently reviewed in Kotlyar et al . 16 . While the field of PPI prediction is methodologically diverse, irrespective of the paradigm, learning algorithm, and scale of the number of predictions, the field has certain fundamental commonalities.…”

Section: Introductionmentioning

confidence: 99%

Reciprocal Perspective for Improved Protein-Protein Interaction Prediction

Dick

Green

2018

Sci Rep

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All protein-protein interaction (PPI) predictors require the determination of an operational decision threshold when differentiating positive PPIs from negatives. Historically, a single global threshold, typically optimized via cross-validation testing, is applied to all protein pairs. However, we here use data visualization techniques to show that no single decision threshold is suitable for all protein pairs, given the inherent diversity of protein interaction profiles. The recent development of high throughput PPI predictors has enabled the comprehensive scoring of all possible protein-protein pairs. This, in turn, has given rise to context, enabling us now to evaluate a PPI within the context of all possible predictions. Leveraging this context, we introduce a novel modeling framework called Reciprocal Perspective (RP), which estimates a localized threshold on a per-protein basis using several rank order metrics. By considering a putative PPI from the perspective of each of the proteins within the pair, RP rescores the predicted PPI and applies a cascaded Random Forest classifier leading to improvements in recall and precision. We here validate RP using two state-of-the-art PPI predictors, the Protein-protein Interaction Prediction Engine and the Scoring PRotein INTeractions methods, over five organisms: Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and Mus musculus. Results demonstrate the application of a post hoc RP rescoring layer significantly improves classification (p < 0.001) in all cases over all organisms and this new rescoring approach can apply to any PPI prediction method.

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“…Efforts to complete protein interactions networks include not only high troughput experimental approaches 7 , but also computational predictive methods, recently reviewed by Kotlyar et al . The latter can be based in sequence features, conservation across species, protein domains, 3D structure, interaction network topology, or a combination of several of the previous data types 8 . To expand the list of known DGs, information systems, like DisGeNet 9 , Open Targets 10 or DISEASES 11 , integrate and weight heterogeneous evidence sources linking genes with diseases, including text-mining approaches.…”

Section: Introductionmentioning

confidence: 99%

Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease analysis

García-Vaquero

Gama‐Carvalho

Rivas

et al. 2018

Sci Rep

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Discovering disease-associated genes (DG) is strategic for understanding pathological mechanisms. DGs form modules in protein interaction networks and diseases with common phenotypes share more DGs or have more closely interacting DGs. This prompted the development of Specific Betweenness (S2B) to find genes associated with two related diseases. S2B prioritizes genes frequently and specifically present in shortest paths linking two disease modules. Top S2B scores identified genes in the overlap of artificial network modules more than 80% of the times, even with incomplete or noisy knowledge. Applied to Amyotrophic Lateral Sclerosis and Spinal Muscular Atrophy, S2B candidates were enriched in biological processes previously associated with motor neuron degeneration. Some S2B candidates closely interacted in network cliques, suggesting common molecular mechanisms for the two diseases. S2B is a valuable tool for DG prediction, bringing new insights into pathological mechanisms. More generally, S2B can be applied to infer the overlap between other types of network modules, such as functional modules or context-specific subnetworks. An R package implementing S2B is publicly available at https://github.com/frpinto/S2B.

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Prediction of Protein‐Protein Interactions

Cited by 21 publications

References 150 publications

Open Data for Differential Network Analysis in Glioma

Open Data for Differential Network Analysis in Glioma

Reciprocal Perspective for Improved Protein-Protein Interaction Prediction

Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease analysis

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