Computational Surprisal Analysis Speeds-Up Genomic Characterization of Cancer Processes
| Autor: | Iaakov Exman, Simcha Simon, Françoise Remacle, Nataly Kravchenko-Balasha, Raphael D. Levine |
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| Rok vydání: | 2014 |
| Předmět: |
Computer and Information Sciences
Transcription Genetic Carcinogenesis Systems biology Computation lcsh:Medicine Network structure Biology Machine learning computer.software_genre Databases 03 medical and health sciences 0302 clinical medicine Software Design Genetics Medicine and Health Sciences Cancer Detection and Diagnosis Humans lcsh:Science 030304 developmental biology 0303 health sciences Multidisciplinary Cell immortalization Genome Human Software Tools Surprisal analysis business.industry lcsh:R Biology and Life Sciences Computational Biology Software Engineering Genomics Method of analysis Computing Methods Gene Expression Regulation Neoplastic Oncology 030220 oncology & carcinogenesis lcsh:Q Artificial intelligence Information Technology business computer Algorithms Research Article |
| Zdroj: | PLoS ONE, Vol 9, Iss 11, p e108549 (2014) PLoS ONE |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0108549 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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