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 (Frontiers in Bioengineering and Biotechnology, 2023)
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 NSCLC 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 RNA (scRNA) sequencing data. This paper compares artificial intelligence (AI) based NSCLC transcriptome data analysis methods divided into target and analysis technology groups. The AI methodologies were schematically categorized so researchers can easily match analysis methods according to their analysis goals. The most widely known and frequently utilized transcriptome analysis goal is to find essential biomarkers and classify carcinomas and cluster NSCLC subtypes. AI-based analysis methods are divided into three major categories: regression, machine learning, and deep learning. Specific models and hybrid or 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.