Pages that link to "Q35051900"

The following pages link to Detecting epistatic effects in association studies at a genomic level based on an ensemble approach (Q35051900):

Displaying 17 items.

• Gradient Boosting as a SNP Filter: an Evaluation Using Simulated and Hair Morphology Data. (Q30729547) (← links)
• Evaluation of a two-stage framework for prediction using big genomic data (Q30912379) (← links)
• A Robust and Efficient Feature Selection Algorithm for Microarray Data (Q31150389) (← links)
• "RADIOTRANSCRIPTOMICS": A synergy of imaging and transcriptomics in clinical assessment (Q33704494) (← links)
• A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets (Q34235767) (← links)
• An integrated approach to reduce the impact of minor allele frequency and linkage disequilibrium on variable importance measures for genome-wide data (Q34357568) (← links)
• Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality (Q34447454) (← links)
• Bayesian neural networks for detecting epistasis in genetic association studies (Q34637032) (← links)
• LEAP: biomarker inference through learning and evaluating association patterns (Q35196550) (← links)
• Detecting genome-wide epistases based on the clustering of relatively frequent items. (Q35630175) (← links)
• Genomic Scans of Zygotic Disequilibrium and Epistatic SNPs in HapMap Phase III Populations (Q35678843) (← links)
• Detecting epistasis in human complex traits (Q38247072) (← links)
• Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe (Q39085025) (← links)
• Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations. (Q41049720) (← links)
• Synthetic sickness or lethality points at candidate combination therapy targets in glioblastoma (Q42597215) (← links)
• Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting (Q49905853) (← links)
• Machine Learning and Radiogenomics: Lessons Learned and Future Directions. (Q55428338) (← links)