Leveraging Subjective Parameters and Biomarkers in Machine Learning Models: The Feasibility of <i>lnc-IL7R</i> for Managing Emphysema Progression

Leveraging Subjective Parameters and Biomarkers in Machine Learning Models: The Feasibility of <i>lnc-IL7R</i> for Managing Emphysema Progression

<b>Background/Objectives:</b> Chronic obstructive pulmonary disease (COPD) remains a leading cause of death worldwide, with emphysema progression providing valuable insights into disease development. Clinical assessment approaches, including pulmonary function tests and high-resolution c...

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Main Authors:	Tzu-Tao Chen, Tzu-Yu Cheng, I-Jung Liu, Shu-Chuan Ho, Kang-Yun Lee, Huei-Tyng Huang, Po-Hao Feng, Kuan-Yuan Chen, Ching-Shan Luo, Chien-Hua Tseng, Yueh-His Chen, Arnab Majumdar, Cheng-Yu Tsai, Sheng-Ming Wu
Format:	Article
Language:	English
Published:	MDPI AG 2025-05-01
Series:	Diagnostics
Subjects:	chronic obstructive pulmonary disease (COPD) emphysema percentage of low attenuation area (LAA%) long non-coding interleukin-7 receptor α-subunit gene (<i>lnc-IL7R</i>) machine learning
Online Access:	https://www.mdpi.com/2075-4418/15/9/1165
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