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 ow...

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Autores principales:	Soohyun Ko, Jonghwan Choi, Jaegyoon Ahn
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2ccc46503e47424fa94cca6e873e2639
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spelling	oai:doaj.org-article:2ccc46503e47424fa94cca6e873e26392021-12-02T14:01:23ZGVES: machine learning model for identification of prognostic genes with a small dataset10.1038/s41598-020-79889-52045-2322https://doaj.org/article/2ccc46503e47424fa94cca6e873e26392021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79889-5https://doaj.org/toc/2045-2322Abstract 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.Soohyun KoJonghwan ChoiJaegyoon AhnNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Soohyun Ko Jonghwan Choi Jaegyoon Ahn GVES: machine learning model for identification of prognostic genes with a small dataset
description	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. 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	article
author	Soohyun Ko Jonghwan Choi Jaegyoon Ahn
author_facet	Soohyun Ko Jonghwan Choi Jaegyoon Ahn
author_sort	Soohyun Ko
title	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_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_sort	gves: machine learning model for identification of prognostic genes with a small dataset
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2ccc46503e47424fa94cca6e873e2639
work_keys_str_mv	AT soohyunko gvesmachinelearningmodelforidentificationofprognosticgeneswithasmalldataset AT jonghwanchoi gvesmachinelearningmodelforidentificationofprognosticgeneswithasmalldataset AT jaegyoonahn gvesmachinelearningmodelforidentificationofprognosticgeneswithasmalldataset
_version_	1718392129536917504

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

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