Accurate prediction of human essential genes using only nucleotide composition and association information.

ABSTRACT:

Motivation

Previously constructed classifiers in predicting eukaryotic essential genes integrated a variety of features including experimental ones. If we can obtain satisfactory prediction using only nucleotide (sequence) information, it would be more promising. Three groups recently identified essential genes in human cancer cell lines using wet experiments and it provided wonderful opportunity to accomplish our idea. Here we improved the Z curve method into the λ-interval form to denote nucleotide composition and association information and used it to construct the SVM classifying model.

Results

Our model accurately predicted human gene essentiality with an AUC higher than 0.88 both for 5-fold cross-validation and jackknife tests. These results demonstrated that the essen

SUBMITTER: Guo FB 

PROVIDER: S-EPMC7110051 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature