TDP‑43 LOF–Driven Splicing Signature as a Molecular Classifier for ALS
- Demonstrate how a TDP‑43 loss‑of‑function–driven alternative splicing signature functions as a mechanism‑based molecular classifier that accurately distinguishes ALS from healthy controls
- Outline the machine‑learning framework built on iPSC and patient spinal‑cord transcriptomes, emphasizing feature interpretability and reproducible performance across independent ALS cohorts
- Highlight the implications of these findings for developing mechanism‑anchored biomarkers and identifying novel therapeutic targets in ALS