Jeff Kiralis

发表

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

Peter C. Andrews, Jason H. Moore, Ryan Amos, 2014, Genetic epidemiology.

Spatially Uniform ReliefF : Increasing the Power to Detect Epistasis in Genetic Association Studies

Jason H. Moore, N. Penrod, C. Greene, 2009 .

Nature-inspired algorithms for the genetic analysis of epistasis in common human diseases: Theoretical assessment of wrapper vs. filter approaches

Jason H. Moore, Casey S. Greene, Jeff Kiralis, 2009, 2009 IEEE Congress on Evolutionary Computation.

Characterizing genetic interactions in human disease association studies using statistical epistasis networks

Ting Hu, Jason H. Moore, Angeline S. Andrew, 2011, BMC Bioinformatics.

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

Jason H. Moore, Casey S. Greene, Jeff Kiralis, 2009, BioData Mining.

GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures

Jason H. Moore, Ryan J. Urbanowicz, Nicholas A. Sinnott-Armstrong, 2012, BioData Mining.

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

Jason H. Moore, Casey S. Greene, Jeff Kiralis, 2010, EvoBIO.

ViSEN: Methodology and Software for Visualization of Statistical Epistasis Networks

Ting Hu, Yuanzhu Chen, Y. Chen, 2013, Genetic epidemiology.

An information-gain approach to detecting three-way epistatic interactions in genetic association studies

Ting Hu, Jason H. Moore, Yuanzhu Peter Chen, 2013, J. Am. Medical Informatics Assoc..

Optimal Use of Biological Expert Knowledge from Literature Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease

Jason H. Moore, Jeff Kiralis, Arvis Sulovari, 2013, EvoBIO.

Complexes with free actions by the group of automorphisms of a free group

Jeff Kiralis, 2006, math/0609419.

A non-Abelian K-theory and pseudo-isotopies of 3-manifolds

Jeff Kiralis, 1996 .

- 1-Application of a Modular Relief Framework for Ranking Genetic Predictors of Disease

Jeff Kiralis, 2011 .

A classification and characterization of two-locus, pure, strict, epistatic models for simulation and detection

Jason H. Moore, Ryan J. Urbanowicz, Jeff Kiralis, 2014, BioData Mining.

Cell-Based Metrics Improve the Detection of Gene-Gene Interactions Using Multifactor Dimensionality Reduction

Jason H. Moore, Nicholas A. Sinnott-Armstrong, Jeff Kiralis, 2013, EvoBIO.

An information-gain approach to detecting three-way epistatic interactions in genetic association studies

Scott M. Williams, Ryan L. Collins, G. Sirugo, 2013, J. Am. Medical Informatics Assoc..

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

Jason H. Moore, Casey S. Greene, Jeff Kiralis, 2009, EvoBIO.

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

Jason H. Moore, Ryan J. Urbanowicz, Jeff Kiralis, 2012, BioData Mining.

Pseudo-isotopies of irreducible 3-manifolds

Jeff Kiralis, 1992 .

GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures

Nicholas A. Sinnott-Armstrong, Jonathan M. Fisher, R. Urbanowicz, 2012, BioData Mining.