Cited 0 times in
Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 조병철 | - |
dc.contributor.author | 표경호 | - |
dc.date.accessioned | 2023-04-20T08:10:12Z | - |
dc.date.available | 2023-04-20T08:10:12Z | - |
dc.date.issued | 2023-02 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/194011 | - |
dc.description.abstract | 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. Copyright © 2023 Joo, Pyo, Chung and Cho. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Frontiers Media S.A. | - |
dc.relation.isPartOf | FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Min Soo Joo | - |
dc.contributor.googleauthor | Kyoung-Ho Pyo | - |
dc.contributor.googleauthor | Jong-Moon Chung | - |
dc.contributor.googleauthor | Byoung Chul Cho | - |
dc.identifier.doi | 10.3389/fbioe.2023.1081950 | - |
dc.contributor.localId | A03822 | - |
dc.contributor.localId | A04809 | - |
dc.relation.journalcode | J04157 | - |
dc.identifier.eissn | 2296-4185 | - |
dc.identifier.pmid | 36873350 | - |
dc.subject.keyword | RNA | - |
dc.subject.keyword | deep learning | - |
dc.subject.keyword | machine learning | - |
dc.subject.keyword | non-small cell lung cancer | - |
dc.subject.keyword | sequence | - |
dc.subject.keyword | statistical analysis | - |
dc.subject.keyword | transcriptome | - |
dc.contributor.alternativeName | Cho, Byoung Chul | - |
dc.contributor.affiliatedAuthor | 조병철 | - |
dc.contributor.affiliatedAuthor | 표경호 | - |
dc.citation.volume | 11 | - |
dc.citation.startPage | 1081950 | - |
dc.identifier.bibliographicCitation | FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, Vol.11 : 1081950, 2023-02 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.