Update README.md

README.md

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 # Serum-MiR-CanPred: An Artificial Intelligence-Driven Framework for Pan-Cancer Prediction Using a Minimal Set of Circulating miRNA Biomarkers
 
 ## Summary
-#TODO
+This study presents Serum-MiR-CanPred, a machine learning framework that leverages serum microRNA (miRNA) expression data to non-invasively diagnose 13 different cancer types. Using a multilayer perceptron (MLP) model and SHAP for interpretability, the method achieved high accuracy (AUC 99.87%) and identified key discriminatory miRNAs, including hsa-miR-5100. Literature validation and molecular docking revealed that AC1MMYR2, a compound targeting the Dicer site, binds stably to pre-miR-5100, suggesting therapeutic potential. This integrative approach demonstrates the dual utility of circulating miRNAs as diagnostic biomarkers and therapeutic targets, offering a promising direction for AI-driven, non-invasive cancer diagnostics and drug discovery.
 
 ## Dataset
 - Source: [GEO Database] (https://www.ncbi.nlm.nih.gov/geo/)
@@ -59,5 +59,6 @@ Overall Accuracy = 96%
 ## Citation
 If using this model, please cite:
 
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 ## License
 MIT License