https://www.selleckchem.com/products/TWS119.html
Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA Splice
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