Predictive modeling of gene expression regulation

Predictive modeling of gene expression regulation

Background: In-depth analysis of regulation networks of genes aberrantly expressed in cancer is essential for better understanding tumors and identifying key genes that could be therapeutically targeted. Results: We developed a quantitative analysis approach to investigate the main biological relati...

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Bibliographic Details
Main Authors: Damia, G. (Author), Fratelli, M. (Author), Ganzinelli, M. (Author), Guffanti, F. (Author), Masseroli, M. (Author), Matteucci, M. (Author), Regondi, C. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
Alkylation
Analysis approach
Biological relationships
Cancer
Diseases
gene expression profiling
Gene Expression Profiling
gene expression regulation
Gene expression regulation
Gene Expression Regulation
Gene Expression Regulation, Neoplastic
gene regulatory network
Gene Regulatory Networks
In-depth analysis
Linear modeling
Machine learning
metabolism
Predictive modeling
Predictive models
Promoter methylation
Regulation networks
Regulatory elements
Regulatory network
reproducibility
Reproducibility of Results
Stem cells
Target genes
Transcription
transcription factor
Transcription Factors
Tumors
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