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DDN / Differential Dependency Network
A caBIG (cancer Biomedical Informatics Grid) analytical tool for detecting and visualizing statistically significant topological changes in transcriptional networks representing two biological conditions. DDN enables differential network analysis and provides an alternative way for defining network biomarkers predictive of phenotypes. DDN also serves as a useful systems biology tool for users across biomedical research communities to infer how genetic, epigenetic or environment variables may affect biological networks and clinical phenotypes.
DINGO / Differential Network Analysis in Genomics
A pathway-based differential network analysis in genomics model for estimating group-specific networks as well as making inference on the differential networks. DINGO jointly estimates the group-specific conditional dependencies by decomposing them into global and group-specific components. The delineation of these components allows for a more refined picture of the major driver and passenger events in the elucidation of cancer progression and development.
A Cytoscape plug-in that visualizes differences among multiple networks. DyNet can be used for analysing how networks change over time or across multiple conditions (dynamic networks). DyNet utilizes Cytoscape’s own built-in network data structure, so there is no need for a specialized file format. Users can just import multiple networks separately as they would normally do and quickly use DyNet to highlight and identify differences that are present. In addition, DyNet also introduces a new method to highlight nodes that are most ‘rewired’. It takes into account actual changes in nodes’ connectivity (their connections to each neighbor separately). Therefore, this method can identify a node that is more strongly connected to different neighbors in different networks, even if its degree or the sum of its edge weights stays the same.
JDINAC / Joint density based non-parametric Differential Interaction Network Analysis and Classification
Identifies differential patterns of network activation between condition-specific groups. JDINAC is an R application that as many advantages. It can (1) achieve differential network analysis and classification simultaneously, (2) adjust confounding factors in the differential network analysis, and (3) it is a nonparametric approach and can identify the nonlinear relationship among variables. Besides, it does not require any conditions on the distribution of the data, which makes it more robust.
INDEED / Integrated DiffErential Expression and Differential network analysis
Builds a sparse differential network based on partial correlation for better visualization, and integrates differential expression (DE) and differential network (DN) analyses for biomarker discovery. INDEED includes four steps: (i) performing DE analysis to obtain p-value for each biomolecule, (ii) building a differential network, (iii) computing the activity score for each biomolecule and, (iv) prioritizing the biomolecules with the activity score. Future work includes developing an R package and extending it to integrate multiple omic data of various types for biomarker discovery.
Diffany / Differential network analysis tool
A generic, ontology-driven framework to infer, visualise and analyse an arbitrary set of condition-specific responses against one reference network. To this end, we have implemented novel ontology-based algorithms that can process highly heterogeneous networks, accounting for both physical interactions and regulatory associations, symmetric and directed edges, edge weights and negation. We propose this integrative framework as a standardised methodology that allows a unified view on differential networks and promotes comparability between differential network studies.
Detects significantly rewired interactions between samples of two groups of condition-specific protein-protein interaction networks (PPINs). PPICompare is a differential PPIN tool that statistically determines significant between-group rewiring events and annotates each rewiring process with the underlying cause. It also constructs a small set of the most relevant alterations to the transcriptome that explain all systematic differences in the networks. It is designed to be used as an extension to PPIXpress, but can also be applied to suitable input data generated in alternative ways.
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