Computational surprisal analysis speeds-up genomic characterization of cancer processes.

ABSTRACT: 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

SUBMITTER: Kravchenko-Balasha N 

PROVIDER: S-EPMC4236016 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature