Learning genetic epistasis using Bayesian network scoring criteria
about
Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA DataFHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search AlgorithmEvaluation of a two-stage framework for prediction using big genomic data.An omnibus permutation test on ensembles of two-locus analyses can detect pure epistasis and genetic heterogeneity in genome-wide association studies.Modeling the altered expression levels of genes on signaling pathways in tumors as causal bayesian networks.A bayesian method for evaluating and discovering disease loci associations.Performance analysis of novel methods for detecting epistasis.A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets.Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality.Genetic studies of complex human diseases: characterizing SNP-disease associations using Bayesian networks.Prediction of P53 mutants (multiple sites) transcriptional activity based on structural (2D&3D) properties.Bayesian neural networks for detecting epistasis in genetic association studiesIdentifying genetic interactions associated with late-onset Alzheimer's disease.LEAP: biomarker inference through learning and evaluating association patternsEvaluating de novo locus-disease discoveries in GWAS using the signal-to-noise ratio.Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences.Discovering causal interactions using Bayesian network scoring and information gain.The metabolomics of asthma control: a promising link between genetics and diseaseA novel artificial neural network method for biomedical prediction based on matrix pseudo-inversionBayesian networks in neuroscience: a survey.epiACO - a method for identifying epistasis based on ant Colony optimization algorithmDynamic network-based epistasis analysis: boolean examples.Synthetic sickness or lethality points at candidate combination therapy targets in glioblastoma.A fast and exhaustive method for heterogeneity and epistasis analysis based on multi-objective optimization.MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies.Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method.
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P2860
Learning genetic epistasis using Bayesian network scoring criteria
description
2011 nî lūn-bûn
@nan
2011 թուականին հրատարակուած գիտական յօդուած
@hyw
2011 թվականին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Learning genetic epistasis using Bayesian network scoring criteria
@ast
Learning genetic epistasis using Bayesian network scoring criteria
@en
Learning genetic epistasis using Bayesian network scoring criteria
@en-gb
Learning genetic epistasis using Bayesian network scoring criteria
@nl
type
label
Learning genetic epistasis using Bayesian network scoring criteria
@ast
Learning genetic epistasis using Bayesian network scoring criteria
@en
Learning genetic epistasis using Bayesian network scoring criteria
@en-gb
Learning genetic epistasis using Bayesian network scoring criteria
@nl
prefLabel
Learning genetic epistasis using Bayesian network scoring criteria
@ast
Learning genetic epistasis using Bayesian network scoring criteria
@en
Learning genetic epistasis using Bayesian network scoring criteria
@en-gb
Learning genetic epistasis using Bayesian network scoring criteria
@nl
P2093
P2860
P3181
P356
P1433
P1476
Learning genetic epistasis using Bayesian network scoring criteria
@en
P2093
Richard E Neapolitan
Shyam Visweswaran
P2860
P2888
P3181
P356
10.1186/1471-2105-12-89
P407
P577
2011-01-01T00:00:00Z
P5875
P6179
1025654271