Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer

Abstract The selection of genes that are important for obtaining gene expression data is challenging. Here, we developed a deep learning-based feature selection method suitable for gene selection. Our novel deep learning model includes an additional feature-selection layer. After model training, the...

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Autores principales:	Yasukuni Mori, Hajime Yokota, Isamu Hoshino, Yosuke Iwatate, Kohei Wakamatsu, Takashi Uno, Hiroki Suyari
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/28bc13331950486aa5c2931065e08487
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spelling	oai:doaj.org-article:28bc13331950486aa5c2931065e084872021-12-02T18:50:53ZDeep learning-based gene selection in comprehensive gene analysis in pancreatic cancer10.1038/s41598-021-95969-62045-2322https://doaj.org/article/28bc13331950486aa5c2931065e084872021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95969-6https://doaj.org/toc/2045-2322Abstract The selection of genes that are important for obtaining gene expression data is challenging. Here, we developed a deep learning-based feature selection method suitable for gene selection. Our novel deep learning model includes an additional feature-selection layer. After model training, the units in this layer with high weights correspond to the genes that worked effectively in the processing of the networks. Cancer tissue samples and adjacent normal pancreatic tissue samples were collected from 13 patients with pancreatic ductal adenocarcinoma during surgery and subsequently frozen. After processing, gene expression data were extracted from the specimens using RNA sequencing. Task 1 for the model training was to discriminate between cancerous and normal pancreatic tissue in six patients. Task 2 was to discriminate between patients with pancreatic cancer (n = 13) who survived for more than one year after surgery. The most frequently selected genes were ACACB, ADAMTS6, NCAM1, and CADPS in Task 1, and CD1D, PLA2G16, DACH1, and SOWAHA in Task 2. According to The Cancer Genome Atlas dataset, these genes are all prognostic factors for pancreatic cancer. Thus, the feasibility of using our deep learning-based method for the selection of genes associated with pancreatic cancer development and prognosis was confirmed.Yasukuni MoriHajime YokotaIsamu HoshinoYosuke IwatateKohei WakamatsuTakashi UnoHiroki SuyariNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yasukuni Mori Hajime Yokota Isamu Hoshino Yosuke Iwatate Kohei Wakamatsu Takashi Uno Hiroki Suyari Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer
description	Abstract The selection of genes that are important for obtaining gene expression data is challenging. Here, we developed a deep learning-based feature selection method suitable for gene selection. Our novel deep learning model includes an additional feature-selection layer. After model training, the units in this layer with high weights correspond to the genes that worked effectively in the processing of the networks. Cancer tissue samples and adjacent normal pancreatic tissue samples were collected from 13 patients with pancreatic ductal adenocarcinoma during surgery and subsequently frozen. After processing, gene expression data were extracted from the specimens using RNA sequencing. Task 1 for the model training was to discriminate between cancerous and normal pancreatic tissue in six patients. Task 2 was to discriminate between patients with pancreatic cancer (n = 13) who survived for more than one year after surgery. The most frequently selected genes were ACACB, ADAMTS6, NCAM1, and CADPS in Task 1, and CD1D, PLA2G16, DACH1, and SOWAHA in Task 2. According to The Cancer Genome Atlas dataset, these genes are all prognostic factors for pancreatic cancer. Thus, the feasibility of using our deep learning-based method for the selection of genes associated with pancreatic cancer development and prognosis was confirmed.
format	article
author	Yasukuni Mori Hajime Yokota Isamu Hoshino Yosuke Iwatate Kohei Wakamatsu Takashi Uno Hiroki Suyari
author_facet	Yasukuni Mori Hajime Yokota Isamu Hoshino Yosuke Iwatate Kohei Wakamatsu Takashi Uno Hiroki Suyari
author_sort	Yasukuni Mori
title	Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer
title_short	Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer
title_full	Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer
title_fullStr	Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer
title_full_unstemmed	Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer
title_sort	deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/28bc13331950486aa5c2931065e08487
work_keys_str_mv	AT yasukunimori deeplearningbasedgeneselectionincomprehensivegeneanalysisinpancreaticcancer AT hajimeyokota deeplearningbasedgeneselectionincomprehensivegeneanalysisinpancreaticcancer AT isamuhoshino deeplearningbasedgeneselectionincomprehensivegeneanalysisinpancreaticcancer AT yosukeiwatate deeplearningbasedgeneselectionincomprehensivegeneanalysisinpancreaticcancer AT koheiwakamatsu deeplearningbasedgeneselectionincomprehensivegeneanalysisinpancreaticcancer AT takashiuno deeplearningbasedgeneselectionincomprehensivegeneanalysisinpancreaticcancer AT hirokisuyari deeplearningbasedgeneselectionincomprehensivegeneanalysisinpancreaticcancer
_version_	1718377493239431168

Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer

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