Nikita A. Sakhanenko

Learn More
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)
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, Emilie S. Fritsch, Manu M. Tekkedil, Simon Anders, Raquel Kuehn +8 others
  • 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)
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)
: We describe some new conceptual tools for the rigorous, mathematical description of the "set-complexity" of graphs. This set-complexity has been shown previously to be a useful measure for analyzing some biological networks, and in discussing biological information in a quantitative fashion. The advances described here allow us to define some significant(More)
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)
Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or phenotypes caused by single gene variants can be identified by established association methods and family-based approaches, complex phenotypic traits resulting(More)
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)