370 research outputs found
GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease-1
No full text<p><b>Copyright information:</b></p><p>Taken from "GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease"</p><p>BMC Bioinformatics 2006;7():39-39.</p><p>Published online 25 Jan 2006</p><p>PMCID:PMC1388239.</p><p>Copyright © 2006 Motsinger et al; licensee BioMed Central Ltd.</p>re the NN inputs
GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease-0
No full text<p><b>Copyright information:</b></p><p>Taken from "GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease"</p><p>BMC Bioinformatics 2006;7():39-39.</p><p>Published online 25 Jan 2006</p><p>PMCID:PMC1388239.</p><p>Copyright © 2006 Motsinger et al; licensee BioMed Central Ltd.</p>e individual values of sex and _234 fill into those nodes. The activation function is a Boolean function AND, thus it will take (61055.5/33038.075)*sex AND (96492.325*11716.425)*_234
Effect of CYP2B6, ABCB1, and CYP3A5 polymorphisms on efavirenz pharmacokinetics and treatment response: an AIDS Clinical Trials Group study
No full textIn AIDS Clinical Trials Group protocols 384, A5095, and A5097s, we characterized relationships between 22 polymorphisms in CYP2B6, ABCB1, and CYP3A5; plasma efavirenz exposure; and/or treatment responses. A stepwise logistic regression procedure selected polymorphisms associated with reduced drug clearance adjusted for body mass index and the composite CYP2B6 516/983 genotype. Relationships between selected polymorphisms and treatment responses were characterized by competing risk methodology. Association analyses involved 821 individuals (317 for pharmacokinetics and 643 for treatment response). Models that included CYP2B6 516/983 genotype best predicted pharmacokinetics. Slow-metabolizer genotypes were associated with increased central nervous system events among white participants and decreased virologic failure among black participants.Heather J. Ribaudo, Huan Liu, Matthias Schwab, Elke Schaeffeler, Michel Eichelbaum, Alison A. Motsinger-Reif, Marylyn D. Ritchie, Ulrich M. Zanger, Edward P. Acosta, Gene D. Morse, Roy M. Gulick, Gregory K. Robbins, David Clifford, and David W. Haa
The effect of alternative permutation testing strategies on the performance of multifactor dimensionality reduction
No full textAbstract Background Multifactor Dimensionality Reduction (MDR) is a novel method developed to detect gene-gene interactions in case-control association analysis by exhaustively searching multi-locus combinations. While the end-goal of analysis is hypothesis generation, significance testing is employed to indicate statistical interest in a resulting model. Because the underlying distribution for the null hypothesis of no association is unknown, non-parametric permutation testing is used. Lately, there has been more emphasis on selecting all statistically significant models at the end of MDR analysis in order to avoid missing a true signal. This approach opens up questions about the permutation testing procedure. Traditionally omnibus permutation testing is used, where one permutation distribution is generated for all models. An alternative is n-locus permutation testing, where a separate distribution is created for each n-level of interaction tested. Findings In this study, we show that the false positive rate for the MDR method is at or below a selected alpha level, and demonstrate the conservative nature of omnibus testing. We compare the power and false positive rates of both permutation approaches and find omnibus permutation testing optimal for preserving power while protecting against false positives. Conclusion Omnibus permutation testing should be used with the MDR method.</p
Faculty Opinions recommendation of XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits.
No full textDiscussion of a Genome‐Wide Association Approach to Determine HIV‐1 Set Point in African Americans
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Embracing Complexity: Searching for Gene-Gene and Gene Environment Interactions in Genetic Epidemiology
No full textIncorporating Concomitant Medications into Linear Models for Genome-Wide Association Studies.
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