Relevant bibliographies by topics / Cytoscape app

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Academic literature on the topic 'Cytoscape app'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Cytoscape app"

1

Lotia, S., J. Montojo, Y. Dong, G. D. Bader, and A. R. Pico. "Cytoscape App Store." Bioinformatics 29, no. 10 (2013): 1350–51. http://dx.doi.org/10.1093/bioinformatics/btt138.

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Demchak, Barry, David Otasek, Alexander R. Pico, et al. "The Cytoscape Automation app article collection." F1000Research 7 (June 20, 2018): 800. http://dx.doi.org/10.12688/f1000research.15355.1.

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Cytoscape is the premiere platform for interactive analysis, integration and visualization of network data. While Cytoscape itself delivers much basic functionality, it relies on community-written apps to deliver specialized functions and analyses. To date, Cytoscape’s CyREST feature has allowed researchers to write workflows that call basic Cytoscape functions, but provides no access to its high value app-based functions. With Cytoscape Automation, workflows can now call apps that have been upgraded to expose their functionality. This article collection is a resource to assist readers in quic
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Pico, Alexander R., Gary D. Bader, Barry Demchak, et al. "The Cytoscape app article collection." F1000Research 3 (July 1, 2014): 138. http://dx.doi.org/10.12688/f1000research.4642.1.

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As a network visualization and analysis platform, Cytoscape relies on apps to provide domain-specific features and functions. There are many resources available to support Cytoscape app development and distribution, including the Cytoscape App Store and an online “cookbook” for app developers. This article collection is another resource to help researchers find out more about relevant Cytoscape apps and to provide app developers with useful implementation tips. The collection will grow over time as new Cytoscape apps are developed and published.
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Morris, John H., Samad Lotia, Allan Wu, et al. "setsApp for Cytoscape: Set operations for Cytoscape Nodes and Edges." F1000Research 3 (August 5, 2015): 149. http://dx.doi.org/10.12688/f1000research.4392.2.

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setsApp (http://apps.cytoscape.org/apps/setsapp) is a relatively simple Cytoscape 3 app for users to handle groups of nodes and/or edges. It supports several important biological workflows and enables various set operations. setsApp provides basic tools to create sets of nodes or edges, import or export sets, and perform standard set operations (union, difference, intersection) on those sets. Automatic set partitioning and layout functions are also provided. The sets functionality is also exposed to users and app developers in the form of a set of commands that can be used for scripting purpos
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Gao, Jianjiong, Chao Zhang, Martijn van Iersel, et al. "BridgeDb app: unifying identifier mapping services for Cytoscape." F1000Research 3 (July 1, 2014): 148. http://dx.doi.org/10.12688/f1000research.4521.1.

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The BridgeDb app for Cytoscape allows users to map and annotate identifiers of genes, proteins and metabolites in the context of biological networks. The app greatly simplifies the identifier mapping process in Cytoscape by providing a unified interface to different mapping resources and services. The app also provides a programming interface via Cytoscape Commands that can be utilized for identifier mapping by other Cytoscape apps. In this article we provide a technical guide to the BridgeDb app for mapping identifiers in Cytoscape.
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Treister, Adam, and Alexander R. Pico. "Identifier Mapping in Cytoscape." F1000Research 7 (August 6, 2018): 725. http://dx.doi.org/10.12688/f1000research.14807.2.

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Identifier Mapping, the association of terms across disparate taxonomies and databases, is a common hurdle in bioinformatics workflows. The idmapper app for Cytoscape simplifies identifier mapping for genes and proteins in the context of common biological networks. This app provides a unified interface to different identifier resources accessible through a right-click on the table's column header. It also provides an OSGi programming interface via Cytoscape Commands and CyREST that can be utilized for identifier mapping in scripts and other Cytoscape apps, and supports integrated Swagger docum
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Huang, Li Jun, Jeffrey N. Law, and T. M. Murali. "Automating the PathLinker app for Cytoscape." F1000Research 7 (June 12, 2018): 727. http://dx.doi.org/10.12688/f1000research.14616.1.

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PathLinker is a graph-theoretic algorithm originally developed to reconstruct the interactions in a signaling pathway of interest. It efficiently computes multiple short paths within a background protein interaction network from the receptors to transcription factors (TFs) in a pathway. Since December 2015, PathLinker has been available as an app for Cytoscape. This paper describes how we automated the app to use the CyRest infrastructure and how users can incorporate PathLinker into their software pipelines.
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Morris, John H., Dhameliya Vijay, Steven Federowicz, Alexander R. Pico, and Thomas E. Ferrin. "CyAnimator: Simple Animations of Cytoscape Networks." F1000Research 4 (August 5, 2015): 482. http://dx.doi.org/10.12688/f1000research.6852.1.

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CyAnimator (http://apps.cytoscape.org/apps/cyanimator) is a Cytoscape app that provides a tool for simple animations of Cytoscape networks. The tool allows you to take a series of snapshots (CyAnimator calls them frames) of Cytoscape networks. For example, the first frame might be of a network shown from a ”zoomed out” viewpoint and the second frame might focus on a specific group of nodes. Once these two frames are captured by the tool, it can animate between them by interpolating the changes in location, zoom, node color, node size, edge thickness, presence or absence of annotations, etc. Th
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Morris, John H., Dhameliya Vijay, Steven Federowicz, Alexander R. Pico, and Thomas E. Ferrin. "CyAnimator: Simple Animations of Cytoscape Networks." F1000Research 4 (December 30, 2015): 482. http://dx.doi.org/10.12688/f1000research.6852.2.

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CyAnimator (http://apps.cytoscape.org/apps/cyanimator) is a Cytoscape app that provides a tool for simple animations of Cytoscape networks. The tool allows you to take a series of snapshots (CyAnimator calls them frames) of Cytoscape networks. For example, the first frame might be of a network shown from a ”zoomed out” viewpoint and the second frame might focus on a specific group of nodes. Once these two frames are captured by the tool, it can animate between them by interpolating the changes in location, zoom, node color, node size, edge thickness, presence or absence of annotations, etc. Th
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Morris, John H., Allan Kuchinsky, Thomas E. Ferrin, and Alexander R. Pico. "enhancedGraphics: a Cytoscape app for enhanced node graphics." F1000Research 3 (July 1, 2014): 147. http://dx.doi.org/10.12688/f1000research.4460.1.

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enhancedGraphics (http://apps.cytoscape.org/apps/enhancedGraphics) is a Cytoscape app that implements a series of enhanced charts and graphics that may be added to Cytoscape nodes. It enables users and other app developers to create pie, line, bar, and circle plots that are driven by columns in the Cytoscape Node Table. Charts are drawn using vector graphics to allow full-resolution scaling.
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Book chapters on the topic "Cytoscape app"

1

Lima, Rosana Pereira Nobre de. "Inflamação no sistema nervoso decorrente da COVID-19 como fator fisiopatológico desmielinizante: um estudo in silico." In Avanços em Genética Humana um panorama do 1° simpósio da LAGH. Instituto Internacional Despertando Vocações, 2024. http://dx.doi.org/10.31692/978-65-88970-44-7.24-25.

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Durante a pandemia de COVID-19, diversas patologias foram citadas como consequência da doença, dentre elas, foram citadas manifestações neurológicas, desde leves, como a anosmia, a graves, como doenças desmielinizantes1,3. A possibilidade de a COVID-19 estar relacionada à desmielinização tem sido demonstrada em alguns relatos de casos3 . Esse fato é preocupante, pois doenças desmielinizantes são extremamente debilitantes e, até o momento, incuráveis. Dessa forma, o presente estudo visa compreender o mecanismo patogênico do SARS-CoV2 no sistema nervoso pela indução da desmielinização mediante a
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Zhang Ying, Liu Zhiping, and Li Jing-song. "Identifying Biomarkers of Hepatocellular Carcinoma Based on Gene Co-Expression Network from High-Throughput Data." In Studies in Health Technology and Informatics. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-830-3-667.

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In this paper, we proposed an approach systematically based on the use of gene co-expression network analyses to identify potential biomarkers for Hepatocellular Carcinoma (HCC). With the analysis of differential gene expression, we first selected candidate genes closely related to HCC from the whole genome on a large scale. By identifying the relationships between each two genes, we built up the gene co-expression network using Cytoscape software. Then the global network was clustered into several sub-modules by Markov Cluster Algorithm (MCL). And, GO-Analysis was carried out for these identi
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