A stacking ensemble deep learning approach to cancer type classification based on TCGA data

Abstract Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most commonly diagnosed cancers among women. Precise classification of cancers into their types is considered a vital p...

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Autores principales:	Mohanad Mohammed, Henry Mwambi, Innocent B. Mboya, Murtada K. Elbashir, Bernard Omolo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/14921c74d88140be99f7c371288091d2
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spelling	oai:doaj.org-article:14921c74d88140be99f7c371288091d22021-12-02T18:49:32ZA stacking ensemble deep learning approach to cancer type classification based on TCGA data10.1038/s41598-021-95128-x2045-2322https://doaj.org/article/14921c74d88140be99f7c371288091d22021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95128-xhttps://doaj.org/toc/2045-2322Abstract Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most commonly diagnosed cancers among women. Precise classification of cancers into their types is considered a vital problem for cancer diagnosis and therapy. In this paper, we proposed a stacking ensemble deep learning model based on one-dimensional convolutional neural network (1D-CNN) to perform a multi-class classification on the five common cancers among women based on RNASeq data. The RNASeq gene expression data was downloaded from Pan-Cancer Atlas using GDCquery function of the TCGAbiolinks package in the R software. We used least absolute shrinkage and selection operator (LASSO) as feature selection method. We compared the results of the new proposed model with and without LASSO with the results of the single 1D-CNN and machine learning methods which include support vector machines with radial basis function, linear, and polynomial kernels; artificial neural networks; k-nearest neighbors; bagging trees. The results show that the proposed model with and without LASSO has a better performance compared to other classifiers. Also, the results show that the machine learning methods (SVM-R, SVM-L, SVM-P, ANN, KNN, and bagging trees) with under-sampling have better performance than with over-sampling techniques. This is supported by the statistical significance test of accuracy where the p-values for differences between the SVM-R and SVM-P, SVM-R and ANN, SVM-R and KNN are found to be p = 0.003, p = < 0.001, and p = < 0.001, respectively. Also, SVM-L had a significant difference compared to ANN p = 0.009. Moreover, SVM-P and ANN, SVM-P and KNN are found to be significantly different with p-values p = < 0.001 and p = < 0.001, respectively. In addition, ANN and bagging trees, ANN and KNN were found to be significantly different with p-values p = < 0.001 and p = 0.004, respectively. Thus, the proposed model can help in the early detection and diagnosis of cancer in women, and hence aid in designing early treatment strategies to improve survival.Mohanad MohammedHenry MwambiInnocent B. MboyaMurtada K. ElbashirBernard OmoloNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-22 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohanad Mohammed Henry Mwambi Innocent B. Mboya Murtada K. Elbashir Bernard Omolo A stacking ensemble deep learning approach to cancer type classification based on TCGA data
description	Abstract Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most commonly diagnosed cancers among women. Precise classification of cancers into their types is considered a vital problem for cancer diagnosis and therapy. In this paper, we proposed a stacking ensemble deep learning model based on one-dimensional convolutional neural network (1D-CNN) to perform a multi-class classification on the five common cancers among women based on RNASeq data. The RNASeq gene expression data was downloaded from Pan-Cancer Atlas using GDCquery function of the TCGAbiolinks package in the R software. We used least absolute shrinkage and selection operator (LASSO) as feature selection method. We compared the results of the new proposed model with and without LASSO with the results of the single 1D-CNN and machine learning methods which include support vector machines with radial basis function, linear, and polynomial kernels; artificial neural networks; k-nearest neighbors; bagging trees. The results show that the proposed model with and without LASSO has a better performance compared to other classifiers. Also, the results show that the machine learning methods (SVM-R, SVM-L, SVM-P, ANN, KNN, and bagging trees) with under-sampling have better performance than with over-sampling techniques. This is supported by the statistical significance test of accuracy where the p-values for differences between the SVM-R and SVM-P, SVM-R and ANN, SVM-R and KNN are found to be p = 0.003, p = < 0.001, and p = < 0.001, respectively. Also, SVM-L had a significant difference compared to ANN p = 0.009. Moreover, SVM-P and ANN, SVM-P and KNN are found to be significantly different with p-values p = < 0.001 and p = < 0.001, respectively. In addition, ANN and bagging trees, ANN and KNN were found to be significantly different with p-values p = < 0.001 and p = 0.004, respectively. Thus, the proposed model can help in the early detection and diagnosis of cancer in women, and hence aid in designing early treatment strategies to improve survival.
format	article
author	Mohanad Mohammed Henry Mwambi Innocent B. Mboya Murtada K. Elbashir Bernard Omolo
author_facet	Mohanad Mohammed Henry Mwambi Innocent B. Mboya Murtada K. Elbashir Bernard Omolo
author_sort	Mohanad Mohammed
title	A stacking ensemble deep learning approach to cancer type classification based on TCGA data
title_short	A stacking ensemble deep learning approach to cancer type classification based on TCGA data
title_full	A stacking ensemble deep learning approach to cancer type classification based on TCGA data
title_fullStr	A stacking ensemble deep learning approach to cancer type classification based on TCGA data
title_full_unstemmed	A stacking ensemble deep learning approach to cancer type classification based on TCGA data
title_sort	stacking ensemble deep learning approach to cancer type classification based on tcga data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/14921c74d88140be99f7c371288091d2
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A stacking ensemble deep learning approach to cancer type classification based on TCGA data

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