SpectralNET – an application for spectral graph analysis and visualization

Forman, Joshua J.; Clemons, Paul A.; Schreiber, Stuart L.; Haggarty, Stephen J.

doi:10.1186/1471-2105-6-260

Cited by 27 publications

(7 citation statements)

References 28 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fortunately, very effective approaches for this have been developed in the past few years, including locally linear embedding (LLE) (24) and Laplacian eigenmaps (LEs) (25). These have received a lot of attention in machine learning and pattern recognition, and we will not seek to review the literature here, except to note that LLE and LEs are beginning to be used in bioinformatics (26).…”

Section: Dimensionality Reduction and Data Explorationmentioning

confidence: 99%

A mathematical tool for exploring the dynamics of biological networks

Barbano

Spivak

Flajolet

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

We have developed a mathematical approach to the study of dynamical biological networks, based on combining large-scale numerical simulation with nonlinear “dimensionality reduction” methods. Our work was motivated by an interest in the complex organization of the signaling cascade centered on the neuronal phosphoprotein DARPP-32 ( d opamine- and c A MP- r egulated p hospho p rotein of molecular weight 32,000 ). Our approach has allowed us to detect robust features of the system in the presence of noise. In particular, the global network topology serves to stabilize the net state of DARPP-32 phosphorylation in response to variation of the input levels of the neurotransmitters dopamine and glutamate, despite significant perturbation to the concentrations and levels of activity of a number of intermediate chemical species. Further, our results suggest that the entire topology of the network is needed to impart this stability to one portion of the network at the expense of the rest. This could have significant implications for systems biology, in that large, complex pathways may have properties that are not easily replicated with simple modules.

show abstract

Section: Dimensionality Reduction and Data Explorationmentioning

confidence: 99%

A mathematical tool for exploring the dynamics of biological networks

Barbano

Spivak

Flajolet

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…VANTED, [42], GENeVis, [43]) and are equipped with high-level functionalities (e.g. network clustering algorithms, [44] dimensionality reduction methods, [45], visualization of extremely large networks, [46], and network module detection, [47]). However, although the above tools excel in network-related functionalities and visualization quality, none of them comes with a complete and yet simple set of bundled background databases (protein-protein interaction, element annotation, functional and pathway associations) in one place, a fact which is essential for the researcher.…”

Section: Discussionmentioning

confidence: 99%

The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases

et al. 2013

View full text Add to dashboard Cite

BackgroundConstant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases.ResultsIn this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease.ConclusionsThe KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner.

show abstract

“… http://www.ariadnegenomics.com/downloads/ PIVOT [ 165 ] Layout algorithms for visualizing protein interactions and families http://acgt.cs.tau.ac.il/pivot/ ProCope [ 166 ] Prediction and evaluation of protein complexes from purification data experiments http://www.bio.ifi.lmu.de/Complexes/ProCope/ ProViz [ 167 ] Visualization and exploration of interaction networks. Gene Ontology and PSI-MI formats supported http://cbi.labri.fr/eng/proviz.htm SpectralNET [ 168 ] Network analysis and visualizations. Scatter plots and dimensionality reduction algorithms https://www.broadinstitute.org/software/spectralnet Tulip [ 169 ] Enables the development of algorithms, visual encodings, interaction techniques, data models and domain-specific visualizations http://tulip.labri.fr/TulipDrupal/ VANESA [ 170 ] Automatic reconstruction and analysis of biological networks and Petri nets based on life-science database information http://agbi.techfak.uni-bielefeld.de/vanesa/ VANTED [ 171 ] Network reconstruction, data visualization, integration of various data types, network simulation http://tinyurl.com/vanted/ yEd Creation of diagrams manually and import external data http://tinyurl.com/yEdGraph/ Web tools for network analysis APID [ 172 ] Unified protein-protein interactions from BIND, BioGRID, DIP, HPRD, IntAct and MINT http://bioinfow.dep.usal.es/apid/ Arcadia [ 173 ] Translates text-based descriptions of biological networks (SBML files) into standardized diagrams (Systems Biology Graphical Notation Process Description maps) …”

Section: Reviewmentioning

confidence: 99%

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future

Pavlopoulos

Malliarakis

Papanikolaou

et al. 2015

GigaSci

View full text Add to dashboard Cite

“Α picture is worth a thousand words.” This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.

show abstract

SpectralNET – an application for spectral graph analysis and visualization

Cited by 27 publications

References 28 publications

A mathematical tool for exploring the dynamics of biological networks

A mathematical tool for exploring the dynamics of biological networks

The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future

Contact Info

Product

Resources

About