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.
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.
Authors: Richard M Neve; Koei Chin; Jane Fridlyand; Jennifer Yeh; Frederick L Baehner; Tea Fevr; Laura Clark; Nora Bayani; Jean-Philippe Coppe; Frances Tong; Terry Speed; Paul T Spellman; Sandy DeVries; Anna Lapuk; Nick J Wang; Wen-Lin Kuo; Jackie L Stilwell; Daniel Pinkel; Donna G Albertson; Frederic M Waldman; Frank McCormick; Robert B Dickson; Michael D Johnson; Marc Lippman; Stephen Ethier; Adi Gazdar; Joe W Gray Journal: Cancer Cell Date: 2006-12 Impact factor: 31.743
Authors: Meikang Qiu; Emil Khisamutdinov; Zhengyi Zhao; Cheryl Pan; Jeong-Woo Choi; Neocles B Leontis; Peixuan Guo Journal: Philos Trans A Math Phys Eng Sci Date: 2013-09-02 Impact factor: 4.226
Authors: Jason E McDermott; Jing Wang; Hugh Mitchell; Bobbie-Jo Webb-Robertson; Ryan Hafen; John Ramey; Karin D Rodland Journal: Expert Opin Med Diagn Date: 2013-01
Authors: Theo A Knijnenburg; Gunnar W Klau; Francesco Iorio; Mathew J Garnett; Ultan McDermott; Ilya Shmulevich; Lodewyk F A Wessels Journal: Sci Rep Date: 2016-11-23 Impact factor: 4.379
Authors: Steven M Hill; Richard M Neve; Nora Bayani; Wen-Lin Kuo; Safiyyah Ziyad; Paul T Spellman; Joe W Gray; Sach Mukherjee Journal: BMC Bioinformatics Date: 2012-05-11 Impact factor: 3.169