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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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
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
Divisions:	Faculty of Science > Centre for Complexity Science Faculty of Science > Statistics
Journal or Publication Title:	Bioinformatics
Publisher:	Oxford University Press
ISSN:	1367-4803
Official Date:	15 January 2009
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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