Machine learning for detecting gene-gene interactions: a review.
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Epistasis--the essential role of gene interactions in the structure and evolution of genetic systemsA survey about methods dedicated to epistasis detectionApplication of machine learning to proteomics data: classification and biomarker identification in postgenomics biology.MicroRNAs control mRNA fate by compartmentalization based on 3' UTR length in male germ cells.Machine learning and data mining in complex genomic data--a review on the lessons learned in Genetic Analysis Workshop 19.Evaporative cooling feature selection for genotypic data involving interactionsIntegrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination.A random forest approach to the detection of epistatic interactions in case-control studies.Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysisMechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemiaEvaluation of random forests performance for genome-wide association studies in the presence of interaction effects.Neural networks for modeling gene-gene interactions in association studiesA Markov blanket-based method for detecting causal SNPs in GWASOn safari to Random Jungle: a fast implementation of Random Forests for high-dimensional dataBioinformatics challenges for genome-wide association studies.FragViz: visualization of fragmented networksGrid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseasesModel-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise.A nonparametric test to detect quantitative trait loci where the phenotypic distribution differs by genotypes.Weighted risk score-based multifactor dimensionality reduction to detect gene-gene interactions in nasopharyngeal carcinomaGene--environment-wide association studies: emerging approachesAn exhaustive, non-euclidean, non-parametric data mining tool for unraveling the complexity of biological systems--novel insights into malaria.bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studiesFEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach.Microfluidic platform for real-time signaling analysis of multiple single T cells in parallel.Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations.The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study.iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies.Transcriptome classification reveals molecular subtypes in psoriasisGAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architecturesImportance measures for epistatic interactions in case-parent trios.Detecting gene-gene interactions that underlie human diseases.Random forests for genetic association studies.A Bayesian hierarchical gene model on latent genotypes for genome-wide association studies.Combining graph and machine learning methods to analyze differences in functional connectivity across sex.Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all!Risk Classification with an Adaptive Naive Bayes Kernel Machine Model.Do little interactions get lost in dark random forests?Bayesian methods for multivariate modeling of pleiotropic SNP associations and genetic risk prediction.A penalized robust semiparametric approach for gene-environment interactions
P2860
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P2860
Machine learning for detecting gene-gene interactions: a review.
description
2006 nî lūn-bûn
@nan
2006 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2006 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2006年の論文
@ja
2006年論文
@yue
2006年論文
@zh-hant
2006年論文
@zh-hk
2006年論文
@zh-mo
2006年論文
@zh-tw
2006年论文
@wuu
name
Machine learning for detecting gene-gene interactions: a review.
@ast
Machine learning for detecting gene-gene interactions: a review.
@en
Machine learning for detecting gene-gene interactions: a review.
@nl
type
label
Machine learning for detecting gene-gene interactions: a review.
@ast
Machine learning for detecting gene-gene interactions: a review.
@en
Machine learning for detecting gene-gene interactions: a review.
@nl
prefLabel
Machine learning for detecting gene-gene interactions: a review.
@ast
Machine learning for detecting gene-gene interactions: a review.
@en
Machine learning for detecting gene-gene interactions: a review.
@nl
P2860
P50
P1476
Machine learning for detecting gene-gene interactions: a review.
@en
P2093
Brett A McKinney
P2860
P356
10.2165/00822942-200605020-00002
P577
2006-01-01T00:00:00Z
P5875
P6179
1046264238