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Automated exploitation of deep learning for cancer patient stratification across multiple types

MOTIVATION: Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in d...

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Autores principales: Sun, Pingping, Fan, Shijie, Li, Shaochuan, Zhao, Yingwei, Lu, Chang, Wong, Ka-Chun, Li, Xiangtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636288/
https://www.ncbi.nlm.nih.gov/pubmed/37934154
http://dx.doi.org/10.1093/bioinformatics/btad654
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author Sun, Pingping
Fan, Shijie
Li, Shaochuan
Zhao, Yingwei
Lu, Chang
Wong, Ka-Chun
Li, Xiangtao
author_facet Sun, Pingping
Fan, Shijie
Li, Shaochuan
Zhao, Yingwei
Lu, Chang
Wong, Ka-Chun
Li, Xiangtao
author_sort Sun, Pingping
collection PubMed
description MOTIVATION: Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks, meanwhile, the optimization and adjustment of the network are usually costly and time consuming. RESULTS: To address such limitations, we proposed a fully automated deep neural architecture search model for diagnosing consensus molecular subtypes from gene expression data (DNAS). The proposed model uses ant colony algorithm, one of the heuristic swarm intelligence algorithms, to search and optimize neural network architecture, and it can automatically find the optimal deep learning model architecture for cancer diagnosis in its search space. We validated DNAS on eight colorectal cancer datasets, achieving the average accuracy of 95.48%, the average specificity of 98.07%, and the average sensitivity of 96.24%, respectively. Without the loss of generality, we investigated the general applicability of DNAS further on other cancer types from different platforms including lung cancer and breast cancer, and DNAS achieved an area under the curve of 95% and 96%, respectively. In addition, we conducted gene ontology enrichment and pathological analysis to reveal interesting insights into cancer subtype identification and characterization across multiple cancer types. AVAILABILITY AND IMPLEMENTATION: The source code and data can be downloaded from https://github.com/userd113/DNAS-main. And the web server of DNAS is publicly accessible at 119.45.145.120:5001.
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spelling pubmed-106362882023-11-11 Automated exploitation of deep learning for cancer patient stratification across multiple types Sun, Pingping Fan, Shijie Li, Shaochuan Zhao, Yingwei Lu, Chang Wong, Ka-Chun Li, Xiangtao Bioinformatics Original Paper MOTIVATION: Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks, meanwhile, the optimization and adjustment of the network are usually costly and time consuming. RESULTS: To address such limitations, we proposed a fully automated deep neural architecture search model for diagnosing consensus molecular subtypes from gene expression data (DNAS). The proposed model uses ant colony algorithm, one of the heuristic swarm intelligence algorithms, to search and optimize neural network architecture, and it can automatically find the optimal deep learning model architecture for cancer diagnosis in its search space. We validated DNAS on eight colorectal cancer datasets, achieving the average accuracy of 95.48%, the average specificity of 98.07%, and the average sensitivity of 96.24%, respectively. Without the loss of generality, we investigated the general applicability of DNAS further on other cancer types from different platforms including lung cancer and breast cancer, and DNAS achieved an area under the curve of 95% and 96%, respectively. In addition, we conducted gene ontology enrichment and pathological analysis to reveal interesting insights into cancer subtype identification and characterization across multiple cancer types. AVAILABILITY AND IMPLEMENTATION: The source code and data can be downloaded from https://github.com/userd113/DNAS-main. And the web server of DNAS is publicly accessible at 119.45.145.120:5001. Oxford University Press 2023-11-02 /pmc/articles/PMC10636288/ /pubmed/37934154 http://dx.doi.org/10.1093/bioinformatics/btad654 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Sun, Pingping
Fan, Shijie
Li, Shaochuan
Zhao, Yingwei
Lu, Chang
Wong, Ka-Chun
Li, Xiangtao
Automated exploitation of deep learning for cancer patient stratification across multiple types
title Automated exploitation of deep learning for cancer patient stratification across multiple types
title_full Automated exploitation of deep learning for cancer patient stratification across multiple types
title_fullStr Automated exploitation of deep learning for cancer patient stratification across multiple types
title_full_unstemmed Automated exploitation of deep learning for cancer patient stratification across multiple types
title_short Automated exploitation of deep learning for cancer patient stratification across multiple types
title_sort automated exploitation of deep learning for cancer patient stratification across multiple types
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636288/
https://www.ncbi.nlm.nih.gov/pubmed/37934154
http://dx.doi.org/10.1093/bioinformatics/btad654
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