In recent years, many computational methods such as regression based approaches have been proposed to discover transcription factor binding sites based on gene expression data.
Besides these two standard approaches to GWAS, many regression-based models for associating phenotype and genotype have been proposed, such as Lasso models (e.g. Kim et al., 2009).
I can do a regression based, I can use regression to project changes from one onto changes in another.
Real Clear Politics' Sean Trende has developed a county-level demographic regression based on these (and a few other related) factors that correctly projected Romney's losses in Alabama and Mississippi.
Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema.
In the following, we compare our L1-norm log linear regression based approach with other regression methods: linear least squares regression, ridge regression, partial least squares regression, and principal component regression.
Most regressions based on this "conditional" approach find general supporting evidence.
One example of this is below-ground root biomass, where many regression equations have been produced to predict live root biomass based on a ratio of the above-ground biomass [64, 65].
Regression equations based on many predictors were developed in the Syrian population.
Recently, many statistical methods based on penalized regressions have been proposed to tackle the so-called "large P and small N" problem.
To avoid these limitations and in order to tackle more realistic scenarios, many methods based on learning techniques were suggested: artificial neuronal networks (ANN), evolving connectionist systems (ECS) [71, 72], statistical learning [73], regression models and so on.
