Jeff Kiralis

According to our database, Jeff Kiralis authored at least 12 papers between 2009 and 2014.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2014

A Classification and Characterization of Two-Locus, Pure, Strict, Epistatic Models for Simulation and Detection.

Ryan J. Urbanowicz, Ambrose Granizo-Mackenzie, Jeff Kiralis, Jason H. Moore

BioData Min., 2014

2013

Research and applications: An information-gain approach to detecting three-way epistatic interactions in genetic association studies.

Ting Hu, Yuanzhu Peter Chen, Jeff Kiralis, Ryan L. Collins, Christian Wejse, Giorgio Sirugo, Scott M. Williams, Jason H. Moore

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.

Arvis Sulovari, Jeff Kiralis, Jason H. Moore

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.

Jonathan M. Fisher, Peter C. Andrews, Jeff Kiralis, Nicholas A. Sinnott-Armstrong, Jason H. Moore

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.

Ryan J. Urbanowicz, Jeff Kiralis, Nicholas A. Sinnott-Armstrong, Tamra Heberling, Jonathan M. Fisher, Jason H. Moore

BioData Min., 2012

Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection.

Ryan J. Urbanowicz, Jeff Kiralis, Jonathan M. Fisher, Jason H. Moore

BioData Min., 2012

2011

Characterizing Genetic Interactions in Human Disease Association Studies Using Statistical Epistasis Networks.

Ting Hu, Nicholas A. Sinnott-Armstrong, Jeff Kiralis, Angeline S. Andrew, Margaret R. Karagas, Jason H. Moore

BMC Bioinform., 2011

2010

The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics.

Casey S. Greene, Daniel S. Himmelstein, Jeff Kiralis, Jason H. Moore

Proceedings of the Evolutionary Computation, 2010

2009

Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions.

Casey S. Greene, Nadia M. Penrod, Jeff Kiralis, Jason H. Moore

BioData Min., 2009

Optimal Use of Expert Knowledge in Ant Colony Optimization for the Analysis of Epistasis in Human Disease.

Casey S. Greene, Jason M. Gilmore, Jeff Kiralis, Peter C. Andrews, Jason H. Moore

Proceedings of the Evolutionary Computation, 2009

An Analysis of New Expert Knowledge Scaling Methods for Biologically Inspired Computing.

Jason M. Gilmore, Casey S. Greene, Peter C. Andrews, Jeff Kiralis, Jason H. Moore

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.

Casey S. Greene, Jeff Kiralis, Jason H. Moore

Proceedings of the IEEE Congress on Evolutionary Computation, 2009