KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies.

ABSTRACT:

Background

Finding epistatic interactions in large association studies like genome-wide association studies (GWAS) with the nowadays-available large volume of genomic data is a challenging and largely unsolved issue. Few previous studies could handle genome-wide data due to the intractable difficulties met in searching a combinatorial explosive search space and statistically evaluating epistatic interactions given a limited number of samples. Our work is a contribution to this field. We propose a novel approach combining K-Nearest Neighbors (KNN) and Multi Dimensional Reduction (MDR) methods for detecting gene-gene interactions as a possible alternative to existing algorithms, e especially in situations where the number of involved determinants is high. After describing the approac

SUBMITTER: Abo Alchamlat S 

PROVIDER: S-EPMC5361736 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature