AI Medical Compendium Journal:
BMC genetics

Showing 1 to 3 of 3 articles

funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model.

BMC genetics
BACKGROUND: Identification of unknown fungal species aids to the conservation of fungal diversity. As many fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be empl...

Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20.

BMC genetics
BACKGROUND: Multiple layers of genetic and epigenetic variability are being simultaneously explored in an increasing number of health studies. We summarize here different approaches applied in the Data Mining and Machine Learning group at the GAW20 t...

Using recursive feature elimination in random forest to account for correlated variables in high dimensional data.

BMC genetics
BACKGROUND: Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact it...