Background: Many studies have been conducted on essentiality prediction in the Saccharomyces
cerevisiae genome, but the accuracy is not as high as those in bacterial or human genomes.
The most frequently used features are Protein-Protein Interaction (PPI) networks combined
with some other features, such as evolutionary conservation, expression level, and protein domain
information. Sequence composition features are the least used features.
Objective: To improve the accuracy of essentiality prediction in the Saccharomyces cerevisiae genome,
we proposed a highly accurate gene essentiality prediction algorithm.
Methods: In this paper, we propose an algorithm based on a linear Support Vector Machine (SVM)
using sequence features only. The variables in this paper are derived from sequence data based on
the w-nucleotide Z curve format without any other information.
Results: After feature selection, the best area under the receiver operating characteristic curve
(AUC) was 0.944 for 5-fold cross-validation. From 1- to 6-nucleotide Z curve variables, feature extraction
can increase the AUC in all cases.
Conclusion: The prediction on sequence composition is only promising, particularly when a feature
filtering method is used, and maybe a good complement for algorithms based on other features.