Predicting genome-wide redundancy using machine learning.

ABSTRACT:

Background

Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here.

Results

Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withho

SUBMITTER: Chen HW 

PROVIDER: S-EPMC2998534 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature