scholarly journals DeDaL: Cytoscape 3.0 app for producing and morphing data-driven and structure-driven network layouts

2015 ◽  
Author(s):  
Urszula Czerwinska ◽  
Laurence Calzone ◽  
Emmanuel Barillot ◽  
Andrei Zinovyev
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Biological Network ◽  
Data Driven ◽  
Multidimensional Data ◽  
High Throughput Data ◽  
Analysis Task ◽  
Structural Network ◽  
Linear Algorithms ◽  
Processing Steps

Visualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistical insights into biological functions. Currently, high-throughput data are usually visualized on top of predefined network layouts which are not always adapted to a given data analysis task. We developed a Cytoscape app which allows to construct biological network layouts based on the data from molecular profiles imported as values of nodes attributes. DeDaL is a Cytoscape 3.0 app which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. Combining these possibilities facilitates creating insightful network layouts representing both structural network features and the correlation patterns in multivariate data. DeDaL is the first method allowing to construct biological network layouts from high-throughput data. DeDaL is freely available for downloading together with step-by-step tutorial at http://bioinfo-out.curie.fr/projects/dedal/.

scholarly journals High-throughput data-driven interface design of high-energy-density polymer nanocomposites

2020 ◽  
Vol 6 (3) ◽  
pp. 573-581 ◽  
Author(s):  
Zhong-Hui Shen ◽  
Yang Shen ◽  
Xiao-Xing Cheng ◽  
Han-Xing Liu ◽  
Long-Qing Chen ◽  
...  
Keyword(s):  
Energy Density ◽  
Polymer Nanocomposites ◽  
High Throughput ◽  
Interface Design ◽  
High Energy ◽  
High Energy Density ◽  
Data Driven ◽  
High Throughput Data

scholarly journals Creating, generating and comparing random network models with NetworkRandomizer

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 2524 ◽  
Author(s):  
Gabriele Tosadori ◽  
Ivan Bestvina ◽  
Fausto Spoto ◽  
Carlo Laudanna ◽  
Giovanni Scardoni
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Quantitative Data ◽  
Random Network ◽  
Network Models ◽  
Information Network ◽  
Statistical Tool ◽  
New Techniques ◽  
High Throughput Data ◽  
Multiplication Algorithm

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

scholarly journals Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks

2012 ◽  
Vol 6 (1) ◽  
pp. 54 ◽  
Author(s):  
Florian Martin ◽  
Ty M Thomson ◽  
Alain Sewer ◽  
David A Drubin ◽  
Carole Mathis ◽  
...  
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
High Throughput Data

scholarly journals Creating, generating and comparing random network models with Network Randomizer

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2524 ◽  
Author(s):  
Gabriele Tosadori ◽  
Ivan Bestvina ◽  
Fausto Spoto ◽  
Carlo Laudanna ◽  
Giovanni Scardoni
Keyword(s):  
Experimental Data ◽  
High Throughput ◽  
Biological Networks ◽  
Random Network ◽  
Network Models ◽  
Information Network ◽  
Statistical Tool ◽  
New Techniques ◽  
High Throughput Data ◽  
Randomized Networks

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. We have developed an app for the Cytoscape platform which allows the creation of randomized networks and the randomization of existing, real networks. Since there is a lack of tools for generating and randomizing networks, our app helps researchers to exploit different, well known random network models which could be used as a benchmark for validating real datasets. We also propose a novel methodology for creating random weighted networks starting from experimental data. Finally the app provides a statistical tool which compares real versus random attributes, in order to validate all the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

scholarly journals High-throughput data-driven analysis of myofiber composition reveals muscle-specific disease and age-associated patterns

2018 ◽  
Author(s):  
Vered Raz ◽  
Yotam Raz ◽  
Davy van de Vijver ◽  
Davide Bindellini ◽  
Maaike van Putten ◽  
...  
Keyword(s):  
High Throughput ◽  
Muscle Function ◽  
Age Groups ◽  
Data Driven ◽  
Muscle Type ◽  
Muscle Involvement ◽  
High Throughput Data ◽  
Myhc Isoforms ◽  
Visual Assessments ◽  
Two Age Groups

Contractile properties of myofibers are dictated by the abundance of myosin heavy chain (MyHC) isoforms. MyHC composition designates muscle function and its alterations could unravel differential muscle involvement in muscular dystrophies and aging. Current analyses are limited to visual assessments in which myofibers expressing multiple MyHC isoforms are prone to misclassifications. As a result, complex patterns and subtle alterations are unidentified. We developed a high-throughput data-driven myofiber analysis to quantitatively describe the variations in myofibers across the muscle. We investigated alterations in myofiber composition between genotypes, two muscles and two age groups. We show that this analysis facilitates the discovery of complex myofiber compositions and its dependency on age, muscle type and genetic conditions.

scholarly journals Proteome-wide inference of protein kinase regulatory circuits

2019 ◽  
Author(s):  
Brandon M. Invergo ◽  
Borgthor Petursson ◽  
David Bradley ◽  
Girolamo Giudice ◽  
Evangelia Petsalaki ◽  
...  
Keyword(s):  
Protein Kinase ◽  
High Throughput ◽  
Intracellular Signaling ◽  
Structural Information ◽  
Data Driven ◽  
High Throughput Data ◽  
Regulatory Circuits ◽  
Human Kinase ◽  
Kinase Specificity ◽  
Full Network

SummaryComplex networks of regulatory relationships between protein kinases comprise a major component of intracellular signaling. Although many kinase-kinase regulatory relationships have been described in detail, these are biased towards well-studied kinases while the majority of possible relationships remains unexplored. Here, we implement data-driven, unbiased methods to predict human kinase-kinase regulatory relationships and whether they have activating or inhibiting effects. We incorporate high-throughput data, kinase specificity profiles, and structural information to produce our predictions. The results successfully recapitulate previously annotated regulatory relationships and can reconstruct known signaling pathways from the ground up. The full network of predictions is relatively sparse, with the vast majority of relationships assigned low probabilities. However, it nevertheless suggests denser modes of inter-kinase regulation than normally considered in intracellular signaling research.

scholarly journals Creating, generating and comparing random network models with NetworkRandomizer

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 2524 ◽  
Author(s):  
Gabriele Tosadori ◽  
Ivan Bestvina ◽  
Fausto Spoto ◽  
Carlo Laudanna ◽  
Giovanni Scardoni
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Quantitative Data ◽  
Random Network ◽  
Network Models ◽  
Information Network ◽  
Statistical Tool ◽  
New Techniques ◽  
High Throughput Data ◽  
Multiplication Algorithm

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

scholarly journals High‐throughput data‐driven analysis of myofiber composition reveals muscle‐specific disease and age‐associated patterns

2018 ◽  
Vol 33 (3) ◽  
pp. 4046-4053 ◽  
Author(s):  
Vered Raz ◽  
Yotam Raz ◽  
Davy Vijver ◽  
Davide Bindellini ◽  
Maaike Putten ◽  
...  
Keyword(s):  
High Throughput ◽  
Data Driven ◽  
High Throughput Data ◽  
Specific Disease

Constructing Simple Biological Networks for Understanding Complex High-Throughput Data in Plants

2015 ◽  
pp. 503-526 ◽  
Author(s):  
Tomás C. Moyano ◽  
Elena A. Vidal ◽  
Orlando Contreras-López ◽  
Rodrigo A. Gutiérrez
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
High Throughput Data

scholarly journals iMDA-BN: Identification of miRNA-Disease Associations based on the Biological Network and Graph Embedding Algorithm

2020 ◽  
Author(s):  
Kai Zheng ◽  
Zhu-Hong You ◽  
Lei Wang
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Domain Knowledge ◽  
Biological Network ◽  
Representation Learning ◽  
Biological Properties ◽  
Disease Association ◽  
Sequence Information ◽  
Network Representation ◽  
Disease Associations

AbstractBenefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce. These methods cover a very limited type of molecular associations and are therefore not suitable for more comprehensive analysis of molecular network representation information. In this study, we propose a computational model based on the Biological network for predicting potential associations between miRNAs and diseases called iMDA-BN. The iMDA-BN has three significant advantages: I) It uses a new method to describe disease and miRNA characteristics which analyzes node representation information for disease and miRNA from the perspective of biological networks. II) It can predict unproven associations even if miRNAs and diseases do not appear in the biological network. III) Accurate description of miRNA characteristics from biological properties based on high-throughput sequence information. The iMDA-BN predictor achieves an AUC of 0.9145 and an accuracy of 84.49% on the miRNA-disease association baseline dataset, and it can also achieve an AUC of 0.8765 and an accuracy of 80.96% when predicting unknown diseases and miRNAs in the biological network. Compared to existing miRNA-disease association prediction methods, iMDA-BN has higher accuracy and the advantage of predicting unknown associations. In addition, 45, 49, and 49 of the top 50 miRNA-disease associations with the highest predicted scores were confirmed in the case studies, respectively.

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