Jeff Kiralis
According to our database1,
Jeff Kiralis
authored at least 12 papers
between 2009 and 2014.
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Bibliography
2014
A Classification and Characterization of Two-Locus, Pure, Strict, Epistatic Models for Simulation and Detection.
BioData Min., 2014
2013
Research and applications: An information-gain approach to detecting three-way epistatic interactions in genetic association studies.
J. Am. Medical Informatics Assoc., 2013
Optimal Use of Biological Expert Knowledge from Literature Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease.
Proceedings of the Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, 2013
Cell-Based Metrics Improve the Detection of Gene-Gene Interactions Using Multifactor Dimensionality Reduction.
Proceedings of the Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, 2013
2012
GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures.
BioData Min., 2012
Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection.
BioData Min., 2012
2011
Characterizing Genetic Interactions in Human Disease Association Studies Using Statistical Epistasis Networks.
BMC Bioinform., 2011
2010
The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics.
Proceedings of the Evolutionary Computation, 2010
2009
Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions.
BioData Min., 2009
Optimal Use of Expert Knowledge in Ant Colony Optimization for the Analysis of Epistasis in Human Disease.
Proceedings of the Evolutionary Computation, 2009
An Analysis of New Expert Knowledge Scaling Methods for Biologically Inspired Computing.
Proceedings of the Advances in Artificial Life. Darwin Meets von Neumann, 2009
Nature-inspired algorithms for the genetic analysis of epistasis in common human diseases: Theoretical assessment of wrapper vs. filter approaches.
Proceedings of the IEEE Congress on Evolutionary Computation, 2009