Article

Phenotype-driven transitions in regulatory network structure

2017-05-25

Abstract excerpt

Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from multiple factors that together functionally perturb the underlying molecular network. Biological networks are known to be highly modular and contain dense “communities” of genes that carry out cellular processes, but these structures change between tissues, during development...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 668d7f7d-6226-5ec3-a3c6-3d51cd24ff30
DOI: 10.1101/142281

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Phenotype-driven transitions in regulatory network structureDOI 10.1101/142281Read

Genetic Interaction Network Interpretation: A Tidy Data Science Perspective.Semantic proximity Read

Identifying Network Perturbation in CancerSemantic proximity Read

Using random walks to identify cancer-associated modules in expression dataSemantic proximity Read

Structure Learning for Hierarchical Regulatory NetworksSemantic proximity Read

A network analysis to identify mediators of germline-driven differences in breast cancer prognosis.Semantic proximity Read

Single-Cell Transcriptomics Unveils Gene Regulatory Network PlasticitySemantic proximity Read

Principles and strategies for developing network models in cancer.Semantic proximity Read

ANIMA: Association Network Integration for Multiscale AnalysisSemantic proximity Read

Exploring regulation in tissues with eQTL networks.Semantic proximity Read

PhenomeScape: a cytoscape app to identify differentially regulated sub-networks using known disease associations.Semantic proximity Read

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs Clustering framework to analyze integrated multi-edge networksSemantic proximity Read

VAN: an R package for identifying biologically perturbed networks via differential variability analysis.Semantic proximity Read