GVES: machine learning model for identification of prognostic genes with a small dataset.

ABSTRACT: Machine learning may be a powerful approach to more accurate identification of genes that may serve as prognosticators of cancer outcomes using various types of omics data. However, to date, machine learning approaches have shown limited prediction accuracy for cancer outcomes, primarily owing to small sample numbers and relatively large number of features. In this paper, we provide a description of GVES (Gene Vector for Each Sample), a proposed machine learning model that can be efficiently leveraged even with a small sample size, to increase the accuracy of identification of genes with prognostic value. GVES, an adaptation of the continuous bag of words (CBOW) model, generates vector representations of all genes for all samples by leveraging gene expression and biological network data. G

SUBMITTER: Ko S 

PROVIDER: S-EPMC7801384 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature