Identifying genetic determinants of complex phenotypes from whole genome sequence data.

ABSTRACT:

Background

A critical goal in biology is to relate the phenotype to the genotype, that is, to find the genetic determinants of various traits. However, while simple monofactorial determinants are relatively easy to identify, the underpinnings of complex phenotypes are harder to predict. While traditional approaches rely on genome-wide association studies based on Single Nucleotide Polymorphism data, the ability of machine learning algorithms to find these determinants in whole proteome data is still not well known.

Results

To better understand the applicability of machine learning in this case, we implemented two such algorithms, adaptive boosting (AB) and repeated random forest (RRF), and developed a chunking layer that facilitates the analysis of whole proteome data. We fi

SUBMITTER: Long GS 

PROVIDER: S-EPMC6558885 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature