Publications & Patents

Publications & Patents

Publication Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide

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작성자 DAAN
조회 798회 작성일 24-02-27 17:58

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Journal Front Bioeng Biotechnol.
Name Joo MS, Pyo KH, Chung JM, Cho BC.
Year 2023

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.

Keywords: RNA; deep learning; machine learning; non-small cell lung cancer; sequence; statistical analysis; transcriptome.

 

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