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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection

Min Soo Joo1, Kyoung-Ho Pyo2,3,4,5, Jong-Moon Chung1,6

  • 1School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea.

Frontiers in Bioengineering and Biotechnology
|March 6, 2023
PubMed
Summary

This study categorizes statistical and artificial intelligence methods for analyzing non-small cell lung cancer (NSCLC) transcriptome data. It aims to provide a framework for researchers to identify biomarkers and subtypes, advancing NSCLC prognosis and treatment strategies.

Keywords:
RNAdeep learningmachine learningnon-small cell lung cancersequencestatistical analysistranscriptome

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Lung cancer, particularly non-small cell lung cancer (NSCLC), has high global incidence and mortality rates.
  • Current NSCLC research emphasizes analyzing post-surgery prognosis and molecular mechanisms using clinical and RNA sequencing data, including single-cell RNA (scRNA) sequencing.
  • Effective transcriptome data analysis is crucial for understanding NSCLC heterogeneity and improving patient outcomes.

Approach:

  • This paper systematically investigates statistical techniques and artificial intelligence (AI) based methods for NSCLC transcriptome data analysis.
  • Methods are categorized into target-driven and technology-driven groups for easy researcher matching based on analytical goals.
  • Transcriptome analysis methodologies are classified into three main categories: statistical analysis, machine learning, and deep learning.

Key Points:

  • Common transcriptome analysis goals include identifying essential biomarkers, classifying carcinomas, and clustering NSCLC subtypes.
  • The study summarizes specific models and ensemble techniques frequently employed in NSCLC analysis.
  • A schematic categorization of methodologies facilitates the selection of appropriate analysis methods for specific research objectives.

Conclusions:

  • This work provides a foundational overview of diverse transcriptome analysis methods applicable to NSCLC research.
  • By converging and linking various analysis techniques, this paper aims to support advanced research in NSCLC.
  • The organized categorization of methods is intended to streamline the process for researchers investigating NSCLC transcriptome data.