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Predicting cellular growth from gene expression signatures
Edoardo M. Airoldi
, Curtis Huttenhower
, David Gresham
, Charles Lu
, Amy A. Caudy
, Maitreya J. Dunham
, James R. Broach
, David Botstein
,
Olga G. Troyanskaya
Computer Science
Lewis-Sigler Institute for Integrative Genomics
Princeton Institute for Computational Science and Engineering
Center for Statistics & Machine Learning
Research output
:
Contribution to journal
›
Article
›
peer-review
86
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Scopus citations
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Keyphrases
Cell Growth
100%
Gene Signature
100%
Cell Proliferation
100%
Growth Rate
66%
Proposed Methodology
33%
Microorganisms
33%
Regulatory Mechanism
33%
Saccharomyces Cerevisiae
33%
Gene Regulatory Network
33%
Multicellular Organism
33%
Small Sets
33%
Complete Set
33%
Expression Level
33%
Biological Condition
33%
Functional Pathways
33%
Regulatory Pathways
33%
Transcription Factor Binding Sites
33%
Cell Culture
33%
Biological Insight
33%
Cancer Development
33%
Changing Environment
33%
Instantaneous Growth Rate
33%
Cellular Physiology
33%
Saccharomyces Bayanus
33%
Schizosaccharomyces Pombe
33%
System Challenges
33%
Correlated Genes
33%
Biological Significance
33%
Quantitative Aspects
33%
Fundamental System
33%
Technological Platform
33%
Balanced Growth
33%
Metazoans
33%
Statistical Methodology
33%
PKA Signaling
33%
Biochemistry, Genetics and Molecular Biology
Gene Expression Profiling
100%
Cell Growth
100%
Cell Proliferation
100%
Transcription Factors
33%
Microorganism
33%
Saccharomyces cerevisiae
33%
Binding Site
33%
Cell Culture
33%
Regulatory Network
33%
Upregulation
33%
Saccharomyces bayanus
33%
Schizosaccharomyces Pombe
33%
Expression Level
33%
Immunology and Microbiology
Cell Growth
100%
Cell Proliferation
100%
Gene Expression Assay
100%
Saccharomyces cerevisiae
33%
Binding Site
33%
Transcription Factors
33%
Upregulation
33%
Cell Culture
33%
Schizosaccharomyces Pombe
33%
Saccharomyces bayanus
33%
Microorganism
33%