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. GVES clusters samples using their gene vectors, and identifies genes that divide samples into good and poor outcome groups for the prediction of cancer outcomes. Because GVES generates gene vectors for each sample, the sample size effect is reduced. We applied GVES to six cancer types and demonstrated that GVES outperformed existing machine learning methods, particularly for cancer datasets with a small number of samples. Moreover, the genes identified as prognosticators were shown to reside within a number of significant prognostic genetic pathways associated with pancreatic cancer.