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Sparse combinatorial inference with an application in cancer biology
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Mukherjee, Sach, Pelech, Steven, Neve, Richard M., Kuo, Wen-Lin, Ziyad, Safiyyah, Spellman, Paul T., Gray, Joe W. and Speed, Terence P.. (2009) Sparse combinatorial inference with an application in cancer biology. Bioinformatics, Vol.25 (No.2). pp. 265-271. ISSN 1367-4803
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Official URL: http://dx.doi.org/10.1093/bioinformatics/btn611
Abstract
Motivation: Combinatorial effects, in which several variables jointly influence an output or response, play an important role in biological systems. In many settings, Boolean functions provide a natural way to describe such influences. However, biochemical data using which we may wish to characterize such influences are usually subject to much variability. Furthermore, in high-throughput biological settings Boolean relationships of interest are very often sparse, in the sense of being embedded in an overall dataset of higher dimensionality. This motivates a need for statistical methods capable of making inferences regarding Boolean functions under conditions of noise and sparsity.
Results: We put forward a statistical model for sparse, noisy Boolean functions and methods for inference under the model. We focus on the case in which the form of the underlying Boolean function, as well as the number and identity of its inputs are all unknown. We present results on synthetic data and on a study of signalling proteins in cancer biology.
| Item Type: | Journal Article | ||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QD Chemistry T Technology > TP Chemical technology Q Science > QH Natural history > QH301 Biology Q Science > QA Mathematics |
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| Divisions: | Faculty of Science > Centre for Complexity Science Faculty of Science > Statistics |
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| Journal or Publication Title: | Bioinformatics | ||||
| Publisher: | Oxford University Press | ||||
| ISSN: | 1367-4803 | ||||
| Official Date: | 15 January 2009 | ||||
| Dates: |
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| Volume: | Vol.25 | ||||
| Number: | No.2 | ||||
| Number of Pages: | 7 | ||||
| Page Range: | pp. 265-271 | ||||
| Identification Number: | 10.1093/bioinformatics/btn611 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Funder: | U. S. Department of Energy, National Institutes of Health, National Cancer Institute, FulbrightAstraZeneca fellowship | ||||
| Grant number: | DEAC0205CH11231, U54 CA 112970, P50 CA 58207 | ||||
| URI: | http://wrap.warwick.ac.uk/id/eprint/28686 |
Data sourced from Thomson Reuters' Web of Knowledge
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