Cargando…
GVES: machine learning model for identification of prognostic genes with a small dataset
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 sm...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7801384/ https://www.ncbi.nlm.nih.gov/pubmed/33431999 http://dx.doi.org/10.1038/s41598-020-79889-5 |
_version_ | 1783635562427580416 |
---|---|
author | Ko, Soohyun Choi, Jonghwan Ahn, Jaegyoon |
author_facet | Ko, Soohyun Choi, Jonghwan Ahn, Jaegyoon |
author_sort | Ko, Soohyun |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7801384 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78013842021-01-12 GVES: machine learning model for identification of prognostic genes with a small dataset Ko, Soohyun Choi, Jonghwan Ahn, Jaegyoon Sci Rep Article 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. Nature Publishing Group UK 2021-01-11 /pmc/articles/PMC7801384/ /pubmed/33431999 http://dx.doi.org/10.1038/s41598-020-79889-5 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Ko, Soohyun Choi, Jonghwan Ahn, Jaegyoon GVES: machine learning model for identification of prognostic genes with a small dataset |
title | GVES: machine learning model for identification of prognostic genes with a small dataset |
title_full | GVES: machine learning model for identification of prognostic genes with a small dataset |
title_fullStr | GVES: machine learning model for identification of prognostic genes with a small dataset |
title_full_unstemmed | GVES: machine learning model for identification of prognostic genes with a small dataset |
title_short | GVES: machine learning model for identification of prognostic genes with a small dataset |
title_sort | gves: machine learning model for identification of prognostic genes with a small dataset |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7801384/ https://www.ncbi.nlm.nih.gov/pubmed/33431999 http://dx.doi.org/10.1038/s41598-020-79889-5 |
work_keys_str_mv | AT kosoohyun gvesmachinelearningmodelforidentificationofprognosticgeneswithasmalldataset AT choijonghwan gvesmachinelearningmodelforidentificationofprognosticgeneswithasmalldataset AT ahnjaegyoon gvesmachinelearningmodelforidentificationofprognosticgeneswithasmalldataset |