Serum-MiR-CanPred: deep learning framework for pan-cancer classification and miRNA-targeted drug discovery.

ABSTRACT: Cancer diagnosis at an early stage is crucial for improving overall health outcomes. However, existing cancer diagnostic techniques are mostly invasive and tend to identify the disease only in its advanced stages. MicroRNAs (miRNAs), which are small non-coding RNAs involved in gene expression regulation, are stable in serum as circulating miRNAs and have potential as non-invasive biomarkers. However, their application in pan-cancer diagnostics and therapeutics is still largely unexplored. We developed Serum-MiR-CanPred, a deep learning framework using a multi-layer perceptron (MLP) trained on serum miRNA expression data from 20,271 samples across 12 cancer types and healthy controls from GEO databases. The model achieves robust pan-cancer classification (AUC = 96.87%, accuracy = 96%) with

SUBMITTER: Patel N 

PROVIDER: S-EPMC12578308 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature