Luigi Cerulo

This study identifies a panel of 20 microRNAs as potential diagnostic biomarkers for breast cancer using feature selection methods and network analysis.

This paper introduces a biologically informed neural network model for predicting drug sensitivity and synergy using multi-omics data, showing improved performance on imbalanced datasets.

The paper introduces a web tool for Gene-Set Enrichment Analysis using the Mann-Witney test, improving computational efficiency and providing prioritization methods for enrichment results.

This study predicts non-coding RNA functions from raw sequence data using deep learning, achieving good accuracy without requiring secondary structure computation.

The paper introduces scTHI, a method to identify ligand-receptor interactions in glioma using single-cell RNA sequencing data from six human datasets.

RGBM identifies transcriptional regulators of glioma subtypes by inferring gene regulatory networks from diverse data sources and outperforming existing methods on benchmark datasets.

The paper introduces an improved method for detecting statistically significant network changes in biological networks, showing better performance on dense random geometric networks and application to glioma cancer regulatory networks.