In silico phenotyping via co-training for improved phenotype prediction from genotype.

ABSTRACT:

Motivation

Predicting disease phenotypes from genotypes is a key challenge in medical applications in the postgenomic era. Large training datasets of patients that have been both genotyped and phenotyped are the key requisite when aiming for high prediction accuracy. With current genotyping projects producing genetic data for hundreds of thousands of patients, large-scale phenotyping has become the bottleneck in disease phenotype prediction.

Results

Here we present an approach for imputing missing disease phenotypes given the genotype of a patient. Our approach is based on co-training, which predicts the phenotype of unlabeled patients based on a second class of information, e.g. clinical health record information. Augmenting training datasets by this type of in silico pheno

SUBMITTER: Roqueiro D 

PROVIDER: S-EPMC4765855 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature