Detecting epistatic effects in association studies at a genomic level based on an ensemble approach
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Gradient Boosting as a SNP Filter: an Evaluation Using Simulated and Hair Morphology Data.Evaluation of a two-stage framework for prediction using big genomic data.A Robust and Efficient Feature Selection Algorithm for Microarray Data."RADIOTRANSCRIPTOMICS": A synergy of imaging and transcriptomics in clinical assessmentA comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets.An integrated approach to reduce the impact of minor allele frequency and linkage disequilibrium on variable importance measures for genome-wide data.Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality.Bayesian neural networks for detecting epistasis in genetic association studiesLEAP: biomarker inference through learning and evaluating association patternsDetecting genome-wide epistases based on the clustering of relatively frequent items.Genomic Scans of Zygotic Disequilibrium and Epistatic SNPs in HapMap Phase III PopulationsGenome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe.Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations.Synthetic sickness or lethality points at candidate combination therapy targets in glioblastoma.Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting.Machine Learning and Radiogenomics: Lessons Learned and Future Directions.
P2860
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P2860
Detecting epistatic effects in association studies at a genomic level based on an ensemble approach
description
2011 nî lūn-bûn
@nan
2011 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հուլիսին 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
Detecting epistatic effects in ...... based on an ensemble approach
@ast
Detecting epistatic effects in ...... based on an ensemble approach
@en
type
label
Detecting epistatic effects in ...... based on an ensemble approach
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Detecting epistatic effects in ...... based on an ensemble approach
@en
prefLabel
Detecting epistatic effects in ...... based on an ensemble approach
@ast
Detecting epistatic effects in ...... based on an ensemble approach
@en
P2860
P356
P1433
P1476
Detecting epistatic effects in ...... based on an ensemble approach
@en
P2093
Benjamin Horstman
Yixuan Chen
P2860
P304
P356
10.1093/BIOINFORMATICS/BTR227
P407
P50
P577
2011-07-01T00:00:00Z