Predicting gene function using few positive examples and unlabeled ones.

ABSTRACT:

Background

A large amount of functional genomic data have provided enough knowledge in predicting gene function computationally, which uses known functional annotations and relationship between unknown genes and known ones to map unknown genes to GO functional terms. The prediction procedure is usually formulated as binary classification problem. Training binary classifier needs both positive examples and negative ones that have almost the same size. However, from various annotation database, we can only obtain few positive genes annotation for most of functional terms, that is, there are only few positive examples for training classifier, which makes predicting directly gene function infeasible.

Results

We propose a novel approach SPE_RNE to train classifier for each functi

SUBMITTER: Chen Y 

PROVIDER: S-EPMC2975410 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature