Erratum: Corrigendum: Protein interaction data curation: the International Molecular Exchange (IMEx) consortium

Orchard, Sandra; Kerrien, Samuel; Abbani, Sara; Aranda, Bruno; Bhate, Jignesh; Bidwell, Shelby L.; Bridge, Alan; Briganti, Leonardo; Brinkman, Fiona S. L.; Chatr‐aryamontri, Andrew; Chautard, Émilie; Chen, Carol; Dumousseau, Marine; Goll, Johannes; Hancock, Robert E. W.; Hannick, Linda; Jurišica, Igor; Khadake, Jyoti; Lynn, David J.; Mahadevan, Usha; Perfetto, Livia; Raghunath, Arathi; Ricard-Blum, S.; Roechert, Bernd; Salwiński, Łukasz; Stümpflen, Volker; Tyers, Mike; Uetz, Peter; Xenarios, Ioannis; Hermjakob, Henning

doi:10.1038/nmeth0612-626a

Cited by 7 publications

(7 citation statements)

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“…To study the interactome, the protein–protein interaction (PPI) network, based on the IMEx [ 33 ] curated database was created. Human data and Uniprot identifiers were used since interactions in mammals are conservative and the bovine interactions are scarcely annotated.…”

Section: Resultsmentioning

confidence: 99%

“…Human data and Uniprot identifiers were used since interactions in mammals are conservative and the bovine interactions are scarcely annotated. Note that interactions found using IMEx [ 33 ] did not distinguish between interactions found in a specific tissue. Interactions between proteins allowed for study of the topological characteristics using MCODE (an application available in Cytoscape).…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the basal transcripts in the ovary found in the Gene Expression Atlas were integrated with the list of genes used to build the PPI network. The IMEx [ 33 ] network (only interactions in humans depicted) resulted in a network composed of 75 nodes and 297 edges. The PPI network contained many other proteins, which did not originate from the initial list.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, the topological characteristics were investigated using MCODE applied to the PPI network. The obtained IMEx [ 33 ] network is shown in Figure 3 .…”

Section: Resultsmentioning

confidence: 99%

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Death Processes in Bovine Theca and Granulosa Cells Modelled and Analysed Using a Systems Biology Approach

McEvoy

Sinderewicz

Creedon

et al. 2021

IJMS

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In this paper, newly discovered mechanisms of atresia and cell death processes in bovine ovarian follicles are investigated. For this purpose the mRNA expression of receptor interacting protein kinases 1 and 3 (RIPK1 and RIPK3) of the granulosa and theca cells derived from healthy and atretic follicles are studied. The follicles were assigned as either healthy or atretic based on the estradiol to progesterone ratio. A statistically significant difference was recorded for the mRNA expression of a RIPK1 and RIPK3 between granulosa cells from healthy and atretic follicles. To further investigate this result a systems biology approach was used. The genes playing roles in necroptosis, apoptosis and atresia were chosen and a network was created based on human genes annotated by the IMEx database in Cytoscape to identify hubs and bottle-necks. Moreover, correlation networks were built in the Cluepedia plug-in. The networks were created separately for terms describing apoptosis and programmed cell death. We demonstrate that necroptosis (RIPK—dependent cell death pathway) is an alternative mechanism responsible for death of bovine granulosa and theca cells. We conclude that both apoptosis and necroptosis occur in the granulosa cells of dominant follicles undergoing luteinisation and in the theca cells from newly selected follicles.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Finally, the topological characteristics were investigated using MCODE applied to the PPI network. The obtained IMEx [ 33 ] network is shown in Figure 3 .…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Death Processes in Bovine Theca and Granulosa Cells Modelled and Analysed Using a Systems Biology Approach

McEvoy

Sinderewicz

Creedon

et al. 2021

IJMS

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“…The PPI data were retrieved from the interactom browser -Mentha [5] (version 2016.09). It limits itself to direct physical PPIs curated by members of the International Molecular Exchange consortium (IMEx) [19]. Each PPI is associated with a reliability score which takes the evidienes such as experimental method, size of experiments and relevant literature into account [5].…”

Section: Experimental Setup 31 Datasetmentioning

confidence: 99%

Bayesian Collective Markov Random Fields for Subcellular Localization Prediction of Human Proteins

Zhu

Ester

2017

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

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Advanced biotechnology makes it possible to access a multitude of heterogeneous proteomic, interactomic, genomic, and functional annotation data. One challenge in computational biology is to integrate these data to enable automated prediction of the Subcellular Localizations (SCL) of human proteins. For proteins that have multiple biological roles, their correct in silico assignment to di erent SCL can be considered as an imbalanced multi-label classi cation problem. In this study, we developed a Bayesian Collective Markov Random Fields (BCMRFs) model for multi-SCL prediction of human proteins. Given a set of unknown proteins and their corresponding protein-protein interaction (PPI) network, the SCLs of each protein can be inferred by the SCLs of its interacting partners. To do so, we integrate PPIs, the adjacency of SCLs and protein features, and perform transductive learning on the re-balanced dataset. Our experimental results show that the spatial adjacency of the SCLs improves multi-SCL prediction, especially for the SCLs with few annotated instances. Our approach outperforms the state-of-art PPIbased and feature-based multi-SCL prediction method for human proteins. KEYWORDSHuman protein subcellular localization; markov random eld; transductive learning; imbalanced multi-label classi cation.

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Two Decades of Biological Pathway Databases: Results and Challenges

Rahmati

Pastrello

Rossos

et al. 2019

Encyclopedia of Bioinformatics and Computational Biology

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Erratum: Corrigendum: Protein interaction data curation: the International Molecular Exchange (IMEx) consortium

Abstract: In the version of this article initially published, the y-axis label on a graph in Figure 1b was incorrect. The error has been corrected in the HTML and PDF versions of the article.

Cited by 7 publications

References 0 publications

Death Processes in Bovine Theca and Granulosa Cells Modelled and Analysed Using a Systems Biology Approach

Death Processes in Bovine Theca and Granulosa Cells Modelled and Analysed Using a Systems Biology Approach

Bayesian Collective Markov Random Fields for Subcellular Localization Prediction of Human Proteins

Two Decades of Biological Pathway Databases: Results and Challenges

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