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Multiple high-throughput genetic interaction studies have provided substantial evidence of modularity in genetic interaction networks. However, the correspondence between these network modules and specific pathways of information flow is often ambiguous. Genetic interaction and molecular interaction analyses have not generated large-scale maps comprising… (More)

- Stefan Wilkening, Gen Lin, +11 authors Lars M. Steinmetz
- Genetics
- 2014

Dissecting the molecular basis of quantitative traits is a significant challenge and is essential for understanding complex diseases. Even in model organisms, precisely determining causative genes and their interactions has remained elusive, due in part to difficulty in narrowing intervals to single genes and in detecting epistasis or linked quantitative… (More)

- Nikita A. Sakhanenko, David J. Galas
- Complexity
- 2011

- Nikita A. Sakhanenko, George F. Luger, Carl R. Stern
- FLAIRS Conference
- 2007

We describe an architecture for representing and managing context shifts that supports dynamic data interpretation. This architecture utilizes two layers of learning and three layers of control for adapting and evolving new stochastic models to accurately represent changing and evolving situations. At the core of this architecture is a form of probabilistic… (More)

- Deepak Kapur, Nikita A. Sakhanenko
- TPHOLs
- 2003

Automatically proving properties of tail-recursive function definitions by induction is known to be challenging. The difficulty arises due to a property of a tail-recursive function definition typically expressed by instantiating the accumulator argument to be a constant only on one side of the property. The application of the induction hypothesis gets… (More)

- Tomasz M. Ignac, Nikita A. Sakhanenko, David J. Galas
- Complexity
- 2012

- David J. Galas, Nikita A. Sakhanenko, Alexander Skupin, Tomasz M. Ignac
- Journal of Computational Biology
- 2014

Context dependence is central to the description of complexity. Keying on the pairwise definition of "set complexity," we use an information theory approach to formulate general measures of systems complexity. We examine the properties of multivariable dependency starting with the concept of interaction information. We then present a new measure for… (More)

- Nikita A. Sakhanenko, David J. Galas
- Journal of Computational Biology
- 2015

Information theory is valuable in multiple-variable analysis for being model-free and nonparametric, and for the modest sensitivity to undersampling. We previously introduced a general approach to finding multiple dependencies that provides accurate measures of levels of dependency for subsets of variables in a data set, which is significantly nonzero only… (More)

- NIKITA A. SAKHANENKO, GEORGE F. LUGER, HANNA E. MAKARUK, JOYSREE B. AUBREY, DAVID B. HOLTKAMP
- 2006

This paper considers a set of shock physics experiments that investigate how materials respond to the extremes of deformation, pressure, and temperature when exposed to shock waves. Due to the complexity and the cost of these tests, the available experimental data set is often very sparse. A support vector machine (SVM) technique for regression is used for… (More)

- Nikita A. Sakhanenko, David J. Galas
- Journal of Computational Biology
- 2012

For the computational analysis of biological problems-analyzing data, inferring networks and complex models, and estimating model parameters-it is common to use a range of methods based on probabilistic logic constructions, sometimes collectively called machine learning methods. Probabilistic modeling methods such as Bayesian Networks (BN) fall into this… (More)