Pages that link to "Item:Q2199193"
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The following pages link to A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility (Q2199193):
Displaying 16 items.
- Detecting essential and removable interactions in genome-wide association studies (Q440073) (← links)
- Information metrics in genetic epidemiology (Q458189) (← links)
- FAACOSE: a fast adaptive ant colony optimization algorithm for detecting SNP epistasis (Q1674812) (← links)
- Building up a robust risk mathematical platform to predict colorectal cancer (Q1688123) (← links)
- An optimization approach to epistasis detection (Q1755406) (← links)
- The geometry of partial fitness orders and an efficient method for detecting genetic interactions (Q1784416) (← links)
- Mining susceptibility gene modules and disease risk genes from SNP data by combining network topological properties with support vector regression (Q1786549) (← links)
- An entropy-based approach for testing genetic epistasis underlying complex diseases (Q1788367) (← links)
- A general framework for and new normalization of attributable proportion (Q2001846) (← links)
- An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria (Q2092177) (← links)
- A computational method to detect epistatic effects contributing to a quantitative trait (Q2196793) (← links)
- Asymptotic distributions of empirical interaction information (Q2241513) (← links)
- A new technique for generating pathogenic barcodes in breast cancer susceptibility analysis (Q2415748) (← links)
- Detecting epistasis via Markov bases (Q2819989) (← links)
- Bayesian epistasis association mapping via SNP imputation (Q3303663) (← links)
- Kernel-based measure of variable importance for genetic association studies (Q6636154) (← links)
