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STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data

TLDR
In the new, fifth release of STITCH, functionality to filter out the proteins and chemicals not associated with a given tissue is implemented and a new network view is implemented that gives the user the ability to view binding affinities of chemicals in the interaction network.
Abstract
Interactions between proteins and small molecules are an integral part of biological processes in living organisms. Information on these interactions is dispersed over many databases, texts and prediction methods, which makes it difficult to get a comprehensive overview of the available evidence. To address this, we have developed STITCH ('Search Tool for Interacting Chemicals') that integrates these disparate data sources for 430 000 chemicals into a single, easy-to-use resource. In addition to the increased scope of the database, we have implemented a new network view that gives the user the ability to view binding affinities of chemicals in the interaction network. This enables the user to get a quick overview of the potential effects of the chemical on its interaction partners. For each organism, STITCH provides a global network; however, not all proteins have the same pattern of spatial expression. Therefore, only a certain subset of interactions can occur simultaneously. In the new, fifth release of STITCH, we have implemented functionality to filter out the proteins and chemicals not associated with a given tissue. The STITCH database can be downloaded in full, accessed programmatically via an extensive API, or searched via a redesigned web interface at http://stitch.embl.de.

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Journal ArticleDOI

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

TL;DR: The latest version of STRING more than doubles the number of organisms it covers, and offers an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input.
Posted Content

Open Graph Benchmark: Datasets for Machine Learning on Graphs

TL;DR: The OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs, indicating fruitful opportunities for future research.
Journal ArticleDOI

The BioGRID interaction database: 2019 update

TL;DR: A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene–phenotype and gene–gene relationships, and captures chemical interaction data, including chemical–protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature.
Journal ArticleDOI

Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data.

TL;DR: Many of the stringApp features are introduced and shown how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface.
Journal ArticleDOI

Modeling polypharmacy side effects with graph convolutional networks.

TL;DR: Decagon is presented, an approach for modeling polypharmacy side effects that develops a new graph convolutional neural network for multirelational link prediction in multimodal networks and can predict the exact side effect, if any, through which a given drug combination manifests clinically.
References
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Journal ArticleDOI

STRING v10: protein–protein interaction networks, integrated over the tree of life

TL;DR: H hierarchical and self-consistent orthology annotations are introduced for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution in the STRING database.
Journal ArticleDOI

Network Medicine: A Network-Based Approach to Human Disease

TL;DR: Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
Journal ArticleDOI

STRING v9.1: protein-protein interaction networks, with increased coverage and integration

TL;DR: The update to version 9.1 of STRING is described, introducing several improvements, including extending the automated mining of scientific texts for interaction information, to now also include full-text articles, and providing users with statistical information on any functional enrichment observed in their networks.
Journal ArticleDOI

The druggable genome

TL;DR: An assessment of the number of molecular targets that represent an opportunity for therapeutic intervention is crucial to the development of post-genomic research strategies within the pharmaceutical industry.
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