Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression

Deep learning has proven advantageous in solving cancer diagnostic or classification problems. However, it cannot explain the rationale behind human decisions. Biological pathway databases provide well-studied relationships between genes and their pathways. As pathways comprise knowledge frameworks...

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Autores principales:	Sangick Park, Eunchong Huang, Taejin Ahn
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning neural networks biological function pathway cancer gene expression Biology (General) QH301-705.5 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/846e8cf8b2b7477e856018cc3446d7fe
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spelling	oai:doaj.org-article:846e8cf8b2b7477e856018cc3446d7fe2021-11-11T16:59:30ZClassification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression10.3390/ijms2221115311422-00671661-6596https://doaj.org/article/846e8cf8b2b7477e856018cc3446d7fe2021-10-01T00:00:00Zhttps://www.mdpi.com/1422-0067/22/21/11531https://doaj.org/toc/1661-6596https://doaj.org/toc/1422-0067Deep learning has proven advantageous in solving cancer diagnostic or classification problems. However, it cannot explain the rationale behind human decisions. Biological pathway databases provide well-studied relationships between genes and their pathways. As pathways comprise knowledge frameworks widely used by human researchers, representing gene-to-pathway relationships in deep learning structures may aid in their comprehension. Here, we propose a deep neural network (PathDeep), which implements gene-to-pathway relationships in its structure. We also provide an application framework measuring the contribution of pathways and genes in deep neural networks in a classification problem. We applied PathDeep to classify cancer and normal tissues based on the publicly available, large gene expression dataset. PathDeep showed higher accuracy than fully connected neural networks in distinguishing cancer from normal tissues (accuracy = 0.994) in 32 tissue samples. We identified 42 pathways related to 32 cancer tissues and 57 associated genes contributing highly to the biological functions of cancer. The most significant pathway was G-protein-coupled receptor signaling, and the most enriched function was the G1/S transition of the mitotic cell cycle, suggesting that these biological functions were the most common cancer characteristics in the 32 tissues.Sangick ParkEunchong HuangTaejin AhnMDPI AGarticledeep learningneural networksbiological functionpathwaycancer gene expressionBiology (General)QH301-705.5ChemistryQD1-999ENInternational Journal of Molecular Sciences, Vol 22, Iss 11531, p 11531 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning neural networks biological function pathway cancer gene expression Biology (General) QH301-705.5 Chemistry QD1-999
spellingShingle	deep learning neural networks biological function pathway cancer gene expression Biology (General) QH301-705.5 Chemistry QD1-999 Sangick Park Eunchong Huang Taejin Ahn Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression
description	Deep learning has proven advantageous in solving cancer diagnostic or classification problems. However, it cannot explain the rationale behind human decisions. Biological pathway databases provide well-studied relationships between genes and their pathways. As pathways comprise knowledge frameworks widely used by human researchers, representing gene-to-pathway relationships in deep learning structures may aid in their comprehension. Here, we propose a deep neural network (PathDeep), which implements gene-to-pathway relationships in its structure. We also provide an application framework measuring the contribution of pathways and genes in deep neural networks in a classification problem. We applied PathDeep to classify cancer and normal tissues based on the publicly available, large gene expression dataset. PathDeep showed higher accuracy than fully connected neural networks in distinguishing cancer from normal tissues (accuracy = 0.994) in 32 tissue samples. We identified 42 pathways related to 32 cancer tissues and 57 associated genes contributing highly to the biological functions of cancer. The most significant pathway was G-protein-coupled receptor signaling, and the most enriched function was the G1/S transition of the mitotic cell cycle, suggesting that these biological functions were the most common cancer characteristics in the 32 tissues.
format	article
author	Sangick Park Eunchong Huang Taejin Ahn
author_facet	Sangick Park Eunchong Huang Taejin Ahn
author_sort	Sangick Park
title	Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression
title_short	Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression
title_full	Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression
title_fullStr	Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression
title_full_unstemmed	Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression
title_sort	classification and functional analysis between cancer and normal tissues using explainable pathway deep learning through rna-sequencing gene expression
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/846e8cf8b2b7477e856018cc3446d7fe
work_keys_str_mv	AT sangickpark classificationandfunctionalanalysisbetweencancerandnormaltissuesusingexplainablepathwaydeeplearningthroughrnasequencinggeneexpression AT eunchonghuang classificationandfunctionalanalysisbetweencancerandnormaltissuesusingexplainablepathwaydeeplearningthroughrnasequencinggeneexpression AT taejinahn classificationandfunctionalanalysisbetweencancerandnormaltissuesusingexplainablepathwaydeeplearningthroughrnasequencinggeneexpression
_version_	1718432188204056576

Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression

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