Document Detail

Computational Surprisal Analysis Speeds-Up Genomic Characterization of Cancer Processes.
MedLine Citation:
PMID:  25405334     Owner:  NLM     Status:  Publisher    
Surprisal analysis is increasingly being applied for the examination of transcription levels in cellular processes, towards revealing inner network structures and predicting response. But to achieve its full potential, surprisal analysis should be integrated into a wider range computational tool. The purposes of this paper are to combine surprisal analysis with other important computation procedures, such as easy manipulation of the analysis results - e.g. to choose desirable result sub-sets for further inspection -, retrieval and comparison with relevant datasets from public databases, and flexible graphical displays for heuristic thinking. The whole set of computation procedures integrated into a single practical tool is what we call Computational Surprisal Analysis. This combined kind of analysis should facilitate significantly quantitative understanding of different cellular processes for researchers, including applications in proteomics and metabolomics. Beyond that, our vision is that Computational Surprisal Analysis has the potential to reach the status of a routine method of analysis for practitioners. The resolving power of Computational Surprisal Analysis is here demonstrated by its application to a variety of cellular cancer process transcription datasets, ours and from the literature. The results provide a compact biological picture of the thermodynamic significance of the leading gene expression phenotypes in every stage of the disease. For each transcript we characterize both its inherent steady state weight, its correlation with the other transcripts and its variation due to the disease. We present a dedicated website to facilitate the analysis for researchers and practitioners.
Nataly Kravchenko-Balasha; Simcha Simon; R D Levine; F Remacle; Iaakov Exman
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-11-18
Journal Detail:
Title:  PloS one     Volume:  9     ISSN:  1932-6203     ISO Abbreviation:  PLoS ONE     Publication Date:  2014  
Date Detail:
Created Date:  2014-11-18     Completed Date:  -     Revised Date:  2014-11-19    
Medline Journal Info:
Nlm Unique ID:  101285081     Medline TA:  PLoS One     Country:  -    
Other Details:
Languages:  ENG     Pagination:  e108549     Citation Subset:  -    
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