Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques

Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques

Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cance...

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Bibliographic Details
Main Authors: Hyuk-Jung Kwon, Ui-Hyun Park, Chul Jun Goh, Dabin Park, Yu Gyeong Lim, Isaac Kise Lee, Woo-Jung Do, Kyoung Joo Lee, Hyojung Kim, Seon-Young Yun, Joungsu Joo, Na Young Min, Sunghoon Lee, Sang-Won Um, Min-Seob Lee
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Cancers
Subjects:
liquid biopsy
multi-omics
machine learning
lung cancer
cell-free DNA
copy number variation
Online Access:https://www.mdpi.com/2072-6694/15/18/4556

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