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Detecting phenotype-driven transitions in regulatory network structure
Megha Padi
, John Quackenbush
Molecular and Cellular Biology
Research output
:
Contribution to journal
›
Article
›
peer-review
15
Scopus citations
Overview
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Dive into the research topics of 'Detecting phenotype-driven transitions in regulatory network structure'. Together they form a unique fingerprint.
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Mathematics
Architecture
61%
Biological Networks
15%
Blood Vessels
15%
Cancer
11%
Community
67%
Comparison Method
14%
Complex Networks
13%
Context
6%
Estrogen Receptor
21%
Fibroblasts
18%
Gene
32%
Genome
12%
Human
30%
Module
44%
Mutation
11%
Network Structure
84%
Ovarian Cancer
17%
Partition
41%
Phenotype
80%
Regulatory Networks
100%
Relevance
10%
Robust Methods
13%
Simulation
7%
Structural Change
15%
Topology
7%
Tumor
11%
Engineering & Materials Science
Blood vessels
26%
Complex networks
22%
Estrogens
37%
Fibroblasts
32%
Genes
64%
Genomics
23%
Tissue
56%
Topology
15%
Tumors
22%
Medicine & Life Sciences
Blood Vessels
9%
Breast
25%
Cellular Structures
17%
Estrogen Receptors
15%
Fibroblasts
12%
Gene Regulatory Networks
18%
Genes
20%
Genome
11%
Multifactorial Inheritance
20%
Mutation
9%
Neoplasms
10%
Oncogenes
15%
Ovarian Neoplasms
15%
Phenotype
40%
Sex Characteristics
15%
Chemical Compounds
Angiogenic
39%
Estrogen
14%
Mutation
11%
Partition
66%
Simulation
8%