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Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide
The incidence and mortality rates of lung cancer are high worldwide, where non-small cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases. Recent non-small cell lung cancer research has been focused on analyzing patient prognosis after surgery and identifying mechanisms in connec...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9975749/ https://www.ncbi.nlm.nih.gov/pubmed/36873350 http://dx.doi.org/10.3389/fbioe.2023.1081950 |
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author | Joo, Min Soo Pyo, Kyoung-Ho Chung, Jong-Moon Cho, Byoung Chul |
author_facet | Joo, Min Soo Pyo, Kyoung-Ho Chung, Jong-Moon Cho, Byoung Chul |
author_sort | Joo, Min Soo |
collection | PubMed |
description | The incidence and mortality rates of lung cancer are high worldwide, where non-small cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases. Recent non-small cell lung cancer research has been focused on analyzing patient prognosis after surgery and identifying mechanisms in connection with clinical cohort and ribonucleic acid (RNA) sequencing data, including single-cell ribonucleic acid (scRNA) sequencing data. This paper investigates statistical techniques and artificial intelligence (AI) based non-small cell lung cancer transcriptome data analysis methods divided into target and analysis technology groups. The methodologies of transcriptome data were schematically categorized so researchers can easily match analysis methods according to their goals. The most widely known and frequently utilized transcriptome analysis goal is to find essential biomarkers and classify carcinomas and cluster NSCLC subtypes. Transcriptome analysis methods are divided into three major categories: Statistical analysis, machine learning, and deep learning. Specific models and ensemble techniques typically used in NSCLC analysis are summarized in this paper, with the intent to lay a foundation for advanced research by converging and linking the various analysis methods available. |
format | Online Article Text |
id | pubmed-9975749 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99757492023-03-02 Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide Joo, Min Soo Pyo, Kyoung-Ho Chung, Jong-Moon Cho, Byoung Chul Front Bioeng Biotechnol Bioengineering and Biotechnology The incidence and mortality rates of lung cancer are high worldwide, where non-small cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases. Recent non-small cell lung cancer research has been focused on analyzing patient prognosis after surgery and identifying mechanisms in connection with clinical cohort and ribonucleic acid (RNA) sequencing data, including single-cell ribonucleic acid (scRNA) sequencing data. This paper investigates statistical techniques and artificial intelligence (AI) based non-small cell lung cancer transcriptome data analysis methods divided into target and analysis technology groups. The methodologies of transcriptome data were schematically categorized so researchers can easily match analysis methods according to their goals. The most widely known and frequently utilized transcriptome analysis goal is to find essential biomarkers and classify carcinomas and cluster NSCLC subtypes. Transcriptome analysis methods are divided into three major categories: Statistical analysis, machine learning, and deep learning. Specific models and ensemble techniques typically used in NSCLC analysis are summarized in this paper, with the intent to lay a foundation for advanced research by converging and linking the various analysis methods available. Frontiers Media S.A. 2023-02-15 /pmc/articles/PMC9975749/ /pubmed/36873350 http://dx.doi.org/10.3389/fbioe.2023.1081950 Text en Copyright © 2023 Joo, Pyo, Chung and Cho. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Joo, Min Soo Pyo, Kyoung-Ho Chung, Jong-Moon Cho, Byoung Chul Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide |
title | Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide |
title_full | Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide |
title_fullStr | Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide |
title_full_unstemmed | Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide |
title_short | Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide |
title_sort | artificial intelligence-based non-small cell lung cancer transcriptome rna-sequence analysis technology selection guide |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9975749/ https://www.ncbi.nlm.nih.gov/pubmed/36873350 http://dx.doi.org/10.3389/fbioe.2023.1081950 |
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