Predict Cancer.
From miRNA.
With AI.

What is microRNA and why does it matter for cancer?

MicroRNAs (miRNAs) are small, non-coding RNA molecules approximately 22 nucleotides in length that regulate gene expression at the post-transcriptional level. They function by binding to complementary sequences in messenger RNAs (mRNAs), leading to translational repression or mRNA degradation.

In cancer, miRNA expression patterns are profoundly dysregulated. Different cancer types exhibit characteristic miRNA "signatures" — specific patterns of up- and down-regulated miRNAs that reflect the underlying biology of the tumor. This makes miRNAs powerful biomarkers for cancer detection, subtype classification, and prognosis.

• miRNAs are stable in serum, plasma, and urine — enabling liquid biopsy
• Expression changes occur early in carcinogenesis, before imaging detects tumors
• Each cancer type has a unique miRNA fingerprint
• Single miRNA profiling experiment can interrogate 600+ markers simultaneously

Integrating multiple layers of molecular data

Cancer is a complex disease driven by alterations across multiple molecular layers. While miRNA provides a powerful signal, combining it with mRNA expression, DNA methylation, copy number variation (CNV), and proteomics data creates a more complete picture of tumor biology.

NeoMiriX's MultiOmicsIntegrator class enables joint analysis of all five data modalities. The platform computes correlation matrices between miRNA and mRNA, runs pathway enrichment analysis, and adjusts p-values using the Benjamini-Hochberg method for multiple testing correction.

The VAE (Variational Autoencoder) component learns a shared 64-dimensional latent space across omics types, enabling imputation of missing modalities and generation of synthetic samples for rare cancer types where training data is limited.

Why an ensemble of 9 models outperforms any single model

No single deep learning architecture captures all aspects of miRNA biology. Transformer models excel at capturing global pairwise relationships across all 623 features simultaneously. Graph Neural Networks model structural relationships between samples. Classical ML models provide calibrated probability estimates from well-understood statistical assumptions.

BioFusionEnsemble combines these complementary strengths through weighted soft-vote aggregation — learned weights that reflect each engine's historical accuracy on held-out validation data. Redis-caching ensures predictions complete in milliseconds after the first run.

The XAI suite — SHAP values, attention rollout, and integrated gradients — provides post-hoc interpretability required for clinical settings, answering the critical question: which miRNAs drove this prediction?