Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Jeff Kiralis"'
Publikováno v:
Genetic Epidemiology
Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that sp
Autor:
Giorgio Sirugo, Yuanzhu Peter Chen, Scott M. Williams, Ting Hu, Jeff Kiralis, Christian Wejse, Jason H. Moore, Ryan L. Collins
Publikováno v:
Journal of the American Medical Informatics Association : JAMIA
Background Epistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and p
Publikováno v:
Genetic Epidemiology. 37:283-285
The non-linear interaction effect among multiple genetic factors, i.e. epistasis, has been recognized as a key component in understanding the underlying genetic basis of complex human diseases and phenotypic traits. Due to the statistical and computa
Autor:
Nicholas A. Sinnott-Armstrong, Jeff Kiralis, Jonathan M. Fisher, Peter C. Andrews, Jason H. Moore
Publikováno v:
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics ISBN: 9783642371882
EvoBIO
EvoBIO
Multifactor Dimensionality Reduction (MDR) is a widely-used data-mining method for detecting and interpreting epistatic effects that do not display significant main effects. MDR produces a reduced-dimensionality representation of a dataset which clas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d47f7518d84bb4f9782b2b4e43c56bfc
https://doi.org/10.1007/978-3-642-37189-9_18
https://doi.org/10.1007/978-3-642-37189-9_18
Publikováno v:
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics ISBN: 9783642371882
EvoBIO
EvoBIO
The fast measurement of millions of sequence variations across the genome is possible with the current technology. As a result, a difficult challenge arise in bioinformatics: the identification of combinations of interacting DNA sequence variations p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::111a7a2f0bc587417bc403ce942b529b
https://doi.org/10.1007/978-3-642-37189-9_12
https://doi.org/10.1007/978-3-642-37189-9_12
Autor:
Jeff Kiralis
Publikováno v:
K-Theory. 10:135-174
Autor:
Nicholas A. Sinnott-Armstrong, Jonathan M. Fisher, Jeff Kiralis, Ryan J. Urbanowicz, Tamra Heberling, Jason H. Moore
Publikováno v:
BioData Mining
Background Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target
Publikováno v:
BioData Mining
BioData Mining, Vol 5, Iss 1, p 15 (2012)
BioData Mining, Vol 5, Iss 1, p 15 (2012)
Background Algorithms designed to detect complex genetic disease associations are initially evaluated using simulated datasets. Typical evaluations vary constraints that influence the correct detection of underlying models (i.e. number of loci, herit
Autor:
Angeline S. Andrew, Jeff Kiralis, Margaret R. Karagas, Jason H. Moore, Ting Hu, Nicholas A. Sinnott-Armstrong
Publikováno v:
BMC Bioinformatics
BMC Bioinformatics, Vol 12, Iss 1, p 364 (2011)
BMC Bioinformatics, Vol 12, Iss 1, p 364 (2011)
Background Epistasis is recognized ubiquitous in the genetic architecture of complex traits such as disease susceptibility. Experimental studies in model organisms have revealed extensive evidence of biological interactions among genes. Meanwhile, st
Publikováno v:
Advances in Artificial Life. Darwin Meets von Neumann ISBN: 9783642213137
ECAL (2)
ECAL (2)
High-throughput genotyping has made genome-wide data on human genetic variation commonly available, however, finding associations between specific variations and common diseases has proven difficult. Individual susceptibility to common diseases likel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b89cf0e868206aebc734a67ad7e763fb
https://doi.org/10.1007/978-3-642-21314-4_36
https://doi.org/10.1007/978-3-642-21314-4_36