Journal article

Sparse combinatorial inference with an application in cancer biology

Sach Mukherjee, Steven Pelech, Richard M Neve, Wen-Lin Kuo, Safiyyah Ziyad, Paul T Spellman, Joe W Gray, Terence P Speed

BIOINFORMATICS | OXFORD UNIV PRESS | Published : 2009

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: W..

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University of Melbourne Researchers

Grants

Awarded by U. S. Department of Energy


Awarded by National Cancer Institute


Awarded by NATIONAL CANCER INSTITUTE


Funding Acknowledgements

Funding: Director, Office of Science, Office of Basic Energy Sciences, of the U. S. Department of Energy ( Contract No. DEAC0205CH11231), National Institutes of Health, National Cancer Institute ( U54 CA 112970, P50 CA 58207 to J. W. G.); FulbrightAstraZeneca fellowship ( to S. M.).