mentha: a resource for browsing integrated protein-interaction networks

Calderone, Alberto; Castagnoli, Luisa; Cesareni, Gianni

doi:10.1038/nmeth.2561

Cited by 314 publications

(271 citation statements)

References 9 publications

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“…While we have only found minor number of PPIs with evidences in MINT [40] and 220 Mentha [41], we found a significant portion of evidence in the STRING [39] database. Table 3 shows the number of evidences found of our evaluation with the STRING [39] 222 database and includes numbers of evidences conforming with our predictions as well as 223 the resulting accuracy.…”

contrasting

confidence: 63%

“…Overall, it appears that every careful independent 39 review of existing methods has unraveled some substantial over-estimates [20][21][22]. One 40 recent method combining profile kernels with Support Vector Machines (SVM) to 41 predict pairs of physical, non-permanent PPIs has tried to avoid all known flaws [23]. 42 However, it still awaits critical assessment from independent experts.…”

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confidence: 99%

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ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes

Tran

Hamp

Rost

2018

Preprint

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confidence: 63%

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confidence: 99%

ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes

Tran

Hamp

Rost

2018

Preprint

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“…Interactions from 5 databases were imported and merged for constructing the PPI network-iRefIndex (Razick, Magklaras and Donaldson, 2008), APID Interactomes (Alonso- López et al, 2016), menthe (Calderone, Castagnoli and Cesareni, 2013), IntAct (Kerrien et al, 2012), InnateDB-all (Breuer et al, 2013). From the initial network, connected components were identified and the largest individual connected component was extracted to generate a separate second network.…”

Section: Protein-protein Interaction Network (Ppi Network) Analysis Fmentioning

confidence: 99%

Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

Ahammad

2018

Preprint

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Myocardial infarction, more commonly known as heart attack, is a huge health problem around the world. It is a result of inadequate blood supply to certain parts of the heart and death of heart muscle cells in that region. Although it has been around for a long time, newer and newer ways of probing myocardial infarction is being followed. Bioinformatics and Systems Biology are relatively recent fields to try to give new insights into myocardial infarction. Following the footsteps of others, this in silico study has tried to chime in on the investigation into myocardial infarction. The study began with the gene expression omnibus dataset uploaded to the NCBI from a whole-genome gene expression analysis carried out at Mayo Clinic in Rochester, Minnesota. From the dataset, differentially expressed genes following first-time myocardial infarction were identified and classified into up-regulated and downregulated ones. Gene Set Enrichment Analysis (GSEA) was carried out on the up-regulated genes which were statistically significant and corresponded with the NCBI generated annotations. Protein-Protein Interaction Network for these genes was constructed. GSEA revealed 5 transcription factors, 5 microRNAs and 3 pathways significantly associated with them. From the Protein-Protein Interaction Network, 6 key proteins (hub nodes) have been identified. These 6 proteins may open a new window of opportunity for the discovery/design of new drugs for mitigating the damage caused by myocardial infarction.

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“…However the con dence of PPIs varies from one to another depending on the method, and the size of experiment etc. [5]. To balance of having a high quality of PPI network and reduce the risk of losing valuable information by removing too many edges, we use the con dence scores of the PPIs to weight our MRFs.…”

Section: Markov Random Field (Mrf) On Protein Scl Predictionmentioning

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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mentha: a resource for browsing integrated protein-interaction networks

Abstract: Systems-level approaches require access to comprehensive\ud genome-wide and proteome-wide databases. A comprehensive\ud resource that archives all published protein-protein interactions\ud (PPIs) is not available. In fact, primary PPI databases capture\ud only a fraction of published data

Cited by 314 publications

References 9 publications

ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes

ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes

Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

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

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