Update README.md

README.md

@@ -22,7 +22,8 @@ This study presents Serum-MiR-CanPred, a machine learning framework that leverag
 ## Dataset
 - Source: [GEO Database] (https://www.ncbi.nlm.nih.gov/geo/)
 - GEO Accession: GSE212211, GSE113740, GSE211692, GSE164174
-- Preprocessing: #TODO
+- Preprocessing: To determine the presence of miRNA, sample signals were compared against a threshold established from blank signals. After removing the top and bottom 5% of blank signal intensities, and the threshold was calculated as the mean plus two standard deviations of the remaining values. For detected miRNA, the mean of the filtered blank signals was subtracted from the sample intensities. Signals that were not detected were assigned a value of 0.1 on a log2 scale. Data normalization was performed using internal control miRNAs (hsa-miR-149-3p, hsa-miR-2861, hsa-miR-4463). Batch effects were adjusted using PyComBat, and the dataset was labelled and combined for further analysis.
+
 
 ## Training Details
 - Number of Layers: 3