A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer.

ABSTRACT: Non-small cell lung cancer (NSCLC), the primary histological form of lung cancer, accounts for about 25%-the highest-of all cancer deaths. As NSCLC is often undetected until symptoms appear in the late stages, it is imperative to discover more effective tumor-associated biomarkers for early diagnosis. Topological data analysis is one of the most powerful methodologies applicable to biological networks. However, current studies fail to consider the biological significance of their quantitative methods and utilize popular scoring metrics without verification, leading to low performance. To extract meaningful insights from genomic data, it is essential to understand the relationship between geometric correlations and biological function mechanisms. Through bioinformatics and network analyses,

SUBMITTER: Wu I 

PROVIDER: S-EPMC10202911 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature