Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions

Abstract

Background: A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such epistatic models present an important analytical challenge, requiring that methods perform not only statistical modeling, but also variable selection to generate testable genetic model hypotheses. This challenge is amplified by recent advances in genotyping technology, as the number of potential predictor variables is rapidly increasing.

GEML: A Grammatical Evolution, Machine Learning Approach to Multi-class Classification

“…DTs have previously been combined with GE in [13]. The algorithm was applied to the binary classification task of detecting gene-gene interactions in genetic association studies.…”

Prediction of the compression ratio for municipal solid waste using decision tree

“…In this study, a decision tree as a new alternative approach was utilized to deliver a new and efficient model for the estimation of waste compression ratio (C’c). The simplicity and applicability on various data type without preprocessing are the advantages of this method (Deodhar and Motsinger-Reif, 2010).…”