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The ConsensusPathDB interaction database: 2013 update

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TLDR
ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases, and has re-implemented the graph visualization feature of Consensus pathDB using the Cytoscape.js library.
Abstract
Knowledge of the various interactions between molecules in the cell is crucial for understanding cellular processes in health and disease. Currently available interaction databases, being largely complementary to each other, must be integrated to obtain a comprehensive global map of the different types of interactions. We have previously reported the development of an integrative interaction database called ConsensusPathDB (http://ConsensusPathDB.org) that aims to fulfill this task. In this update article, we report its significant progress in terms of interaction content and web interface tools. ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases. Binary protein interactions are scored with our confidence assessment tool, IntScore. The ConsensusPathDB web interface allows users to take advantage of these integrated interaction and pathway data in different contexts. Recent developments include pathway analysis of metabolite lists, visualization of functional gene/metabolite sets as overlap graphs, gene set analysis based on protein complexes and induced network modules analysis that connects a list of genes through various interaction types. To facilitate the interactive, visual interpretation of interaction and pathway data, we have re-implemented the graph visualization feature of ConsensusPathDB using the Cytoscape.js library.

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Gene Set Knowledge Discovery with Enrichr.

TL;DR: Enrichr as discussed by the authors is a gene set search engine that enables the querying of hundreds of thousands of annotated gene sets Enrichr uniquely integrates knowledge from many high-profile projects to provide synthesized information about mammalian genes and gene sets.
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NicheNet: modeling intercellular communication by linking ligands to target genes

TL;DR: NicheNet is presented, a method that predicts ligand–target links between interacting cells by combining their expression data with prior knowledge on signaling and gene regulatory networks, and can infer active ligands and their gene regulatory effects on interacting cells.
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Cytoscape.js: a graph theory library for visualisation and analysis

TL;DR: Cytoscape.js is an open-source JavaScript-based graph library that can be used to render interactive graphs in a web browser and in a headless manner, useful for graph operations on a server, such as Node.js.
References
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Hiroaki Kitano
- 01 Mar 2002 - 
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Journal ArticleDOI

Innovation: Metabolomics: the apogee of the omics trilogy

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Journal ArticleDOI

DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs

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