Document Detail

A neural network based approach for inference and verification of transcriptional regulatory interactions.
MedLine Citation:
PMID:  17946339     Owner:  NLM     Status:  MEDLINE    
In this paper, we present a comprehensive neural network based modeling and validation framework for reverse engineering gene regulatory interactions. We employ two approaches, Gene Set Stochastic Sampling and Sensitivity Analysis, to infer these interactions. We first apply these methods to a simulated artificial dataset to ensure their correctness and accuracy. True biological interactions are then modeled by analyzing a rat hippocampus development dataset. Finally, we present a thorough computational methodology to test the validity and robustness of the inferred regulations through novel assemblies of relevant testing datasets.
S Knott; S Mostafavi; P Mousavi
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  1     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2006  
Date Detail:
Created Date:  2007-10-23     Completed Date:  2008-02-29     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  5838-41     Citation Subset:  IM    
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MeSH Terms
Bayes Theorem
Computational Biology / methods*
Gene Expression Profiling
Gene Regulatory Networks*
Hippocampus / metabolism*
Models, Genetic
Models, Theoretical
Neural Networks (Computer)*
Pattern Recognition, Automated
Reproducibility of Results
Sensitivity and Specificity
Stochastic Processes
Time Factors

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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